Note: Descriptions are shown in the official language in which they were submitted.
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Gene Expression Profiling for Identification, Monitoring,
and Treatment of Lupus Erythematosus
REFERENCE TO RELATED APPLICATIONS
This application claims the benefit of U.S. Provisional Application No.
60/886,566 filed
January 25, 2007, the contents of which are incorporated by reference in its
entirety.
FIELD OF THE INVENTION
The present invention relates generally to the identification of biological
markers
associated with the identification of lupus erythematosus. More specifically,
the present
invention relates to the use of gene expression data in the identification,
monitoring and
lo treatment of lupus erythematosus and in the characterization and evaluation
of conditions
induced by or related to lupus erythematosus.
BACKGROUND OF THE INVENTION
Lupus, also called erythematosus is a chronic autoimmune disease that is
potentially
debilitating and sometimes fatal as the immune system attacks the body's cells
and tissue,
resulting in inflanunation and tissue damage. Lupus can affect any part of the
body, but most
often harms the heart, joints, skin, lungs, blood vessels, liver, kidneys and
nervous system.
There are several types of lupus, including systemic lupus and cutaneous
lupus. Systemic lupus
erythematosus ("SLE") is the most common type of lupus. It can affect any
system or organ in
the body including the joints, skin, lungs, heart, blood, kidney, or nervous
system. Symptoms of
SLE can range from being a minor inconvenience to very serious and even life
threatening. For
example, a person may experience no pain or they may experience extreme pain,
especially in
the joints. There may be no skin manifestations or there may be rashes that
are disfiguring. They
may have no organ involvement or there may be extreme organ damage.
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Cutaneous lupus primarily affects the skin, but may also involve the hair and
mucous
membranes. Within lupus of the skin, there are different types that cause
different looking rashes
and symptoms. The different types include acute cutaneous lupus erythematosus
("ACLE"),
subacute cutaneous lupus erythematosus ("SCLE"), and chronic cutaneous lupus
erythematosus,
also known as discoid lupus erythematosus ("DLE"). Other terms to describe
specific forms of
discoid (chronic) lupus erythematosus include lupus erythematosus tumidus.
Discoid lupus (DLE) causes red, scaly, coin-shaped lesions on the body
(discoid lesions)
which occur mainly on cheeks and nose but can occur on the upper back, neck,
backs of hands,
lips or scalp. The lesions often leave permanent scars and may cause permanent
scarring hair loss
if the lesions occur on the scalp. They also cause ulcers and scaling if they
occur on the lips.
SCLE is sometimes described as a disease midway between SLE and DLE, and can
coexist with
both SLE and DLE. SCLE causes dry, symmetrical, ring-shaped, superficial
lesions which last
from weeks to months, and sometimes years. SCLE lesions can occur all over the
body, but
typically typically appear on the neck, back and front of the trunk, and arms.
It may also be quite
scaly and resemble psoriasis but does not usually itch. Other symptoms of both
DLE and SCLE
include alopecia, mouth ulcerations, fever, and malaise, myalgia, and
arthritis.
Lupus erythematosus tumidus (LET) is a rare subtype of DLE. Clinically, LET
presents
as smooth, shiny, red-violet plaques of the head and neck that may be pruritic
and have a fine
scale. These lesions characteristically clear without scarring and recur in
their original
distribution. Histologic features include superficial and deep
lymphohistiocytic infiltrates and
abundant dermal deposits of mucin.
A majority of people affected by cutaneous lupus are also extremely
photosensitive.
Cutaneous lupus does not affect any of the internal body organs. Approximately
10-20% of
patients with cutaneous lupus will go on to develop the more serious form of
the disease,
systemic lupus (SLE). However, these cutaneous forms of lupus may occur
independently of
SLE.
Because systemic lupus mimcs several other diseases and its symptoms are
diverse, it is a
very difficult disease to diagnose. Diagnosis of the various types of cutenous
lupus
erythematosus is typically accomplished by performing a biopsy of the affected
skin.
Examination of a small sample of the affected skin under the microscope allows
for a more
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definite diagnosis as the microscopic tissue changes are characteristic. In
addition, a small
sample may be obtained for an immunofluorescence test. Since lupus
erythematosus is a
condition in which there is antibody production to self-tissues, serologic
testing may also be used
in conjunction with a skin biopsy to diagnose lupus. However, serologic
testing alone may not be
a reliable tool to detect cutaneous forms of lupus. Often, an anti-nuclear
antibody (ANA) test for
discoid patients is negative. However, some patients have a low-titre
positive. Approximately
70% of people affected by SCLE also have a positve test for anti-Ro (SSA).
The various cutaneous manifestations of lupus erythematosus can be severe,
leading to
significant pigmentary disturbance and disfigurement, a significant cosmetic
concern among
those affected with the disease. Early detection makes the disease more
manageable, and leads to
a reduction in scarring and pigmentary disturbance. There is currently no
early detection test for
lupus. Because of the limited screening methods available to detect cutaneous
lupus and the
significant physical disfigurement that can result from the disease, a need
exists for better ways
to detect the disease at an early stage and monitor the progression of lupus.
Additionally, information on any condition of a particular patient and a
patient's response
to types and dosages of therapeutic or nutritional agents has become an
important issue in
clinical medicine today not only from the aspect of efficiency of medical
practice for the health
care industry but for improved outcomes and benefits for the patients.
Currently, there are no
known biomarkers predictive of response to therapy in patients afflicted with
lupus. Thus, there
is the need for tests which can aid in monitoring the progression and
treatment of lupus
SUMMARY OF THE INVENTION
The invention is in based in part upon the identification of gene expression
profiles
(Precision Profiles'T') associated with lupus. These genes are referred to
herein as lupus
associated genes. More specifically, the invention is based upon the
surprising discovery that
detection of as few as two lupus associated genes in a subject derived sample
is capable of
identifying individuals with or without lupus with at least 75% accuracy. More
particularly, the
invention is based upon the discovery that the methods provided by the
invention are capable of
detecting lupus by assaying blood samples.
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In various aspects the invention provides methods of evaluating the presence
or absence
(e.g., diagnosing or prognosing) of lupus, based on a smple from the subject,
the sample
providing a source of RNAs, and determining a quantitative measure of the
amount of at least
one constituent of any constituent (e.g., a lupus associated gene) of any of
Tables 1-7, 9-13, and
15-20, and arriving at a measure of each constituent. In a particular
embodiment, the invention
provides a method for evaluating the presence of lupus in a subject based on a
sample from the
subject, the sample providing a source of RNAs, comprising: a) determining a
quantitative
measure of the amount of at least one constituent of any constituent of any
one table selected
from Tables 1-7, 9-13, and 15-20 as a distinct RNA constituent in the subject
sample, wherein
such measure is obtained under measurement conditions that are substantially
repeatable and the
constituent is selected so that measurement of the constituent distinguishes
between a normal
subject and a lupus disease-diagnosed subject in a reference population with
at least 75%
accuracy; and b) comparing the quantitative measure of the constituent in the
subject sample to a
reference value.
Also.provided by the invention is a method for assessing or monitoring the
response to
therapy (e.g., individuals who will respond to a particular therapy
("responders), individuals who
won't respond to a particular therapy ("non-responders"), and/or individuals
in which toxicity of
a particular therapeutic may be an issue), in a subject having lupus or a
condition related to
lupus, based on a sample from the subject, the sample providing a source of
RNAs, the method
comprising: i) determining a quantitative measure of the amount of at least
one constituent of
any panel of constituents in Tables 1-7, 9-13, and 15-20 as a distinct RNA
constituent, wherein
such measure is obtained under measurement conditions that are substantially
repeatable to
produce a patient data set; and ii) comparing the patient data set to a
baseline profile data set,
wherein the baseline profile data set is related to lupus, or condition
related to lupus.
In a further aspect, the invention provides a method for monitoring the
progression of
lupus or a condition related to lupus in a subject, based on a sample from the
subject, the sample
providing a source of RNAs, the method comprising: a) determining a
quantitative measure of
the amount of at least one constituent of any constituent of Tables 1-7, 9-13,
and 15-20 as a
distinct RNA constituent in a sample obtained at a first period of time to
produce a first patient
data set; and determining a quantitative measure of the amount of at least one
constituent of any
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constituent of Tables 1-7, 9-13, and 15-20, as a distinct RNA constituent in a
sample obtained at
a second period of time to produce a second profile data set, wherein such
measurements are
obtained under measurement conditions that are substantially repeatable.
Optionally, the
constituents measured in the first sample are the same constituents measured
in the second
sample. The first subject data set and the second subject data set are
compared allowing the
progression of lupus in a subject to be determined. The second subject sample
is taken e.g., one
day, one week, one month, two months, three months, 1 year, 2 years, or more
after first subject
sample.
In various aspects the invention provides a method for determining a profile
data set, i.e.,
a lupus disease profile, for characterizing a subject with lupus or conditions
related to lupus
based on a sample from the subject, the sample providing a source of RNAs, by
using
amplification for measuring the amount of RNA in a panel of constituents
including at least ons
constituent from any of Tables 1-7, 9-13, and 15-20, and arriving at a measure
of each
constituent. The profile data set contains the measure of each constituent of
the panel.
Also provided by the invention is a method of characterizing lupus or
conditions related
to lupus in a subject, based on a sample from the subject, the sample
providing a source of
RNAs, by assessing a profile data set of a plurality of members, each member
being a
quantitative measure of the amount of a distinct RNA constituent in a panel of
constituents
selected so that measurement of the constituents enables characterization of
lupus.
In yet another aspect the invention provides a method of characterizing lupus
or
conditions related to lupus in a subject, based on a sample from the subject,
the sample providing
a source of RNAs, by determining a quantitative measure of the amount of at
least one
constituent from Tables 1-7, 9-13, and 15-20.
Additionally, the invention includes a biomarker for predicting individual
response to
lupus treatment in a subject having lupus or a condition related to lupus
comprising at least one
constituent of any constituent of Tables 1-7, 9-13, and 15-20.
The methods of the invention further include comparing the quantitative
measure of the
constituent in the subject derived sample to a reference value or a baseline
value, e.g. baseline
data set. The reference value is for example an index value. Comparison of the
subject
measurements to a reference value allows for the present or absence of lupus
to be determined,
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response to therapy to be monitored or the progression of lupus to be
determined. For example,
a similarity in the subject data set compared to a baseline data set derived
from a subject having
lupus indicates the presence of lupus or response to therapy that is not
efficacious. Whereas a
similarity in the subject data set compares to a baseline data set derived
from a subject not
having lupus indicates the absence of lupus or response to therapy that is
efficacious. In various
embodiments, the baseline data set is derived from one or more other samples
from the same
subject, taken when the subject is in a biological condition different from
that in which the
subject was at the time the first sample was taken, with respect to at least
one of age, nutritional
history, medical condition, clinical indicator, medication, physical activity,
body mass, and
environmental exposure, and the baseline profile data set may be derived from
one or more other
samples from one or more different subjects.
The baseline profile data set may be derived from one or more other samples
from the
same subject taken under circumstances different from those of the first
sample, and the
circumstances may be selected from the group consisting of (i) the time at
which the first sample
is taken (e.g., before, after, or during treatment for lupus), (ii) the site
from which the first
sample is taken, (iii) the biological condition of the subject when the first
sample is taken.
The measure of the constituent is increased or decreased in the subject
compared to the
expression of the constituent in the reference, e.g., normal reference sample
or baseline value.
The measure is increased or decreased 10%, 25%, 50% compared to the reference
level.
Alternately, the measure is increased or decreased 1, 2, 5 or more fold
compared to the reference
level.
In various aspects of the invention the methods are carried out wherein the
measurement
conditions are substantially repeatable, particularly within a degree of
repeatability of better than
ten percent, five percent or more particularly within a degree of
repeatability of better than three
percent, and/or wherein efficiencies of amplification for all constituents are
substantially similar,
more particularly wherein the efficiency of amplification is within ten
percent, more particularly
wherein the efficiency of amplification for all constituents is within five
percent, and still more
particularly wherein the efficiency of amplification for all constituents is
within three percent or
less.
In addition, the one or more different subjects may have in common with the
subject at
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least one of age group, gender, ethnicity, geographic location, nutritional
history, medical
condition, clinical indicator, medication, physical activity, body mass, and
environmental
exposure. A clinical indicator may be used to assess lupus or condition
related to lupus of the one
or more different subjects, and may also include interpreting the calibrated
profile data set in the
context of at least one other clinical indicator, wherein the at least one
other clinical indicator
includes blood chemistry, molecular markers in the blood, and physical
findings.
The panel of constituents are selected so as to distinguish from a normal and
a lupus
disease-diagnosed subject. Alternatively, the panel of constituents is
selected as to permit
characterizing the severity of lupus in relation to a normal subject over time
so as to track
movement toward normal as a result of successful therapy and away from normal
in response to
lupus recurrence. Thus, in some embodiments, the methods of the invention are
used to
determine efficacy of treatment of a particular subject.
Preferably, the panel of constituents are selected so as to distinguish, e.g.,
classify
between a normal and a lupus-diagnosed subject with at least 75%, 80%, 85%,
90%, 95%, 97%,
98%, 99% or greater accuracy. By "accuracy" is meant that the method has the
ability to
distinguish, e.g., classify, between subjects having lupus or conditions
associated with lupus, and
those that do not. Accuracy is determined for example by comparing the results
of the Gene
Precision Profiling"' to standard accepted clinical methods of diagnosing
lupus, e.g., one or more
symptoms of cutaneous lupus such as red, scaly, coin-shaped scarring lesions
(discoid lesions);
dry, symmetrical, ring-shaped, superficial non-scarring lesions; smooth,
shiny, red-violet pruritic
plaques with lymphohistiocytic infiltrates and/or dermal deposits of mucin, on
the cheeks, nose,
upper back, neck, lips or scalp.
At least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, or more constituents
are measured. In
one aspect, one or more constituents from Tables 1-7, 9-13, and 15-20 are
measured. In another
aspect, 2 or more constituents from Tables 1-7, 9-13, and 15-20 are measured.
Optimally, the
panel of constituents measured comprises LGALS3BP, IFI6, OASL, PLSCRI,
SERPINGI,
CCL2, TRIM21, THBS1, CALR, NFKB1, ICAM1, CCRIO, FCAR, IL6ST, FCGRIA, CD68,
SGK, BSTl, IL6, IL32, FCGR2B, IL4, IL1B, TLR4, CR1, and CXCR3. Preferably the
following 1, 2, and/or 3 genes are measured, 1) THBSI and IFI6; 2) OASL and
one or more
constituents selected from IL6 and THBS1; 3) SERPING1 and FCGRIA; 4) LGALS3BP
and
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one or more constituents selected from SGK, CCR10, TNFRSF5, CCL2, IL6ST, SSB,
TNFSF5,
and IL3RA, optionally further including one or more constituents selected from
IFI6, OASL,
SERPINGI, CCL2, MMP9, THBS1, SSB, TNF, TRIM21, IFNG; 5) PLSCRI and one or more
constituents selected from FCGR2B, TNFRSF5, and SGK, optionally further
including one or
more constituents selected from TNFRSF5, LGALS3BP, CALR, and FCAR; 6) CCL2 and
one
or more constituents selected from TRIM21, THBS1, SGK, TNF, and TNFRSF5,
optionally
further including one or more constituents selected from CD68, TNF, TNFRSF5,
IL3RA,
FCGR2B, SSB, SGK, IL3RA, CR1,IVIlVP9, FCAR, IL1B, BST1, ICAM1, TLR4, NFKB1,
CALR, CXCR3, FCGRIA, and TNFRSF6; 7) IL6ST and one or more constituents
selected from
SGK, CCR10, and THBS 1, optionally furkher including one or more constituents
selected from
THBS1, CALR, CRI, and IVIlVIP9; 8) NFKB1 and one or more constituents selected
from SGK,
CCR10, IFI6, CCL2, and IL1B, optionally further including one or moie
constituents selected
from CCL2, IFI6, TRIM21, IL1B, TLR4, FCGR2B, BST1, CRl, MMP9, IL18, FCAR,
ICAM1,
OASL, and PLSCRI; and 9) CALR and one or more constituents selected from SGK,
CCR10,
IL18, IFI6, and CCL2, optionally further including one or more constituents
selected from
ILGST, CCR10, TROVE2, CCL2, IFI6, TNF, IL18, and BST1.
In some embodiments, the methods of the present invention are used in
conjunction with
standard accepted clinical methods to diagnose lupus. By lupus or conditions
related to lupus is
meant a chronic inflammatory disease that can affect various parts of the
body, especially the
skin, joints, blood, and kidneys.The term lupus encompasses systemic lupus
erythematosus, the
various forms of cutaneous lupus erythematosus (acute, subacute, and discoid
(including lupus
timidus and hypertrophic variant)), drug induced lupus, and neonatal lupus.
The sample is any sample derived from a subject which contains RNA. For
example the
sample is blood, a blood fraction, body fluid, a population of cells or tissue
from the subject.
Optionally one or more other samples can be taken over an interval of time
that is at least one
month between the first sample and the one or more other samples, or taken
over an interval of
time that is at least twelve months between the first sample and the one or
more samples, or they
may be taken pre-therapy intervention or post-therapy intervention. In such
embodiments, the
first sample may be derived from blood and the baseline profile data set may
be derived from
tissue or body fluid of the subject other than blood. Alternatively, the first
sample is derived from
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tissue or bodily fluid of the subject and the baseline profile data set is
derived from blood.
Also included in the invention are kits for the detection of lupus in a
subject, containing
at least one reagent for the detection or quantification of any constituent
measured according to
the methods of the invention and instructions for using the kit.
Unless otherwise defined, all technical and scientific terms used herein have
the same
meaning as commonly understood by one of ordinary skill in the art to which
this invention
belongs. Although methods and materials similar or equivalent to those
described herein can be
used in the practice or testing of the present invention, suitable methods and
materials are
described below. All publications, patent applications, patents, and other
references mentioned
herein are incorporated by reference in their entirety. In case of conflict,
the present
specification, including definitions, will control. In addition, the
materials, methods, and
examples are illustrative only and not intended to be limiting.
Other features and advantages of the invention will be apparent from the
following
detailed description and claims.
BRIEF DESCRIPTION OF THE DRAWINGS
Figure 1 is a graphical representation of the 2-gene model LGALS3BP and SGK
based
on the Precision Profile'~`" for Lupus (Table 1), capable of distinguishing
between subjects
afflicted with discoid lupus erythematosus (DLE), subacute cutaneous lupus
erythematosus
(SCLE), and lupus tumidus erythematosus (LET) from healthy study volunteers
(HV) and
Source MDx normal subjects (Normal). LGALS3BP values are plotted along the Y-
axis, SGK
values are plotted along the X-axis.
Figure 2 is a graphical representation of the 2-gene model THBS1 and IFI6,
based on the
Precision ProfileT`" for Lupus (Table 1), capable of distinguishing between
subjects afflicted with
discoid lupus erythematosus (DLE), subacute cutaneous lupus erythematosus
(SCLE), and lupus
tumidus erythematosus (LET) from healthy studv volunteers (HV) and Source NIDx
normal
subjects (Normal). THBS1 values are plotted along the Y-axis, IFI6 values are
plotted along the
X-axis.
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Figure 3 is a graphical representation of the 2-gene model OASL and IL6, based
on the
Precision Profile"" for Lupus (Table 1), capable of distinguishing between
subjects afflicted with
lupus (combined population of discoid lupus erythematosus (DLE), subacute
cutaneous lupus
erythematosus (SCLE)), from non-lupus subjects (combined population of healthy
study
volunteers (HV) and Source 1VIDx normal subjects (Normal)). OASL values are
plotted along the
Y-axis, IL6 values are are plotted along the X-axis.
Figure 4 is a graphical representation of the 2 gene model OASL and THBS1,
based on
the Precision ProfileT' for Lupus (Table 1), capable of distinguishing between
subjects afflicted
with discoid lupus erythematosus (DLE), and subacute cutaneous lupus
erythematosus (SCLE),
from healthy study volunteers (HV) and Source MDx normal subjects (Normal).
OASL values
are plotted along the Y-axis, THBS 1 values are are plotted along the X-axis.
DETAILED DESCRIPTION
Definitions
The following terms shall have the meanings indicated unless the context
otherwise
requires:
"Accuracy" refers to the degree of conformity of a measured or calculated
quantity (a test
reported value) to its actual (or true) value. Clinical accuracy relates to
the proportion of true
outcomes (true positives (TP) or true negatives (TN)) versus misclassified
outcomes (false
positives (FP) or false negatives (FN)), and may be stated as a sensitivity,
specificity, positive
predictive values (PPV) or negative predictive values (NPV), or as a
likelihood, odds ratio,
among other measures.
"Algorithm" is a set of rules for describing a biological condition. The rule
set may be
defined exclusively algebraically but may also include alternative or multiple
decision points
requiring domain-specific knowledge, expert interpretation or other clinical
indicators.
An "agent" is a "composition" or a "stimulus", as those terms are defined
herein, or a
combination of a composition and a stimulus.
"Amplification" in the context of a quantitative RT-PCR assay is a function of
the number
of DNA replications that are required to provide a quantitative determination
of its concentration.
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"Amplification" here refers to a degree of sensitivity and specificity of a
quantitative assay
technique. Accordingly, amplification provides a measurement of concentrations
of constituents
that is evaluated under conditions wherein the efficiency of amplification and
therefore the
degree of sensitivity and reproducibility for measuring all constituents is
substantially similar.
A "baseline profile data set" is a set of values associated with constituents
of a Gene
Expression Panel (Precision ProfileTM) resulting from evaluation of a
biological sample (or
population or set of samples) under a desired biological condition that is
used for mathematically
normative purposes. The desired biological condition may be, for example, the
condition of a
subject (or population or set of subjects) before exposure to an agent or in
the presence of an
untreated disease or in the absence of a disease. Alternatively, or in
addition, the desired
biological condition may be health of a subject or a population or set of
subjects. Alternatively,
or in addition, the desired biological condition may be that associated with a
population or set of
subjects selected on the basis of at least one of age group, gender,
ethnicity, geographic location,
nutritional history, medical condition, clinical indicator, medication,
physical activity, body
mass, and environmental exposure.
A "biological condition" of a subject is the condition of the subject in a
pertinent realm
that is under observation, and such realm may include any aspect of the
subject capable of being
monitored for change in condition, such as health; disease including lupus;
ocular disease;
cancer; trauma; aging; infection; tissue degeneration; developmental steps;
physical fitness;
obesity, and mood. As can be seen, a condition in this context may be chronic
or acute or simply
transient. Moreover, a targeted biological condition may be manifest
throughout the organism or
population of cells or may be restricted to a specific organ (such as skin,
heart, eye or blood), but
in either case, the condition may be monitored directly by a sample of the
affected population of
cells or indirectly by a sample derived elsewhere from the subject. The term
"biological
condition" includes a "physiological condition".
"Body.fluid" of a subject includes blood, urine, spinal fluid, lymph, mucosal
secretions,
prostatic fluid, semen, haemolymph or any other body fluid known in the art
for a subject.
"Calibrated profile data set" is a function of a member of a first profile
data set and a
corresponding member of a baseline profile data set for a given constituent in
a panel.
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A "clinical indicator" is any physiological datum used alone or in conjunction
with other
data in evaluating the physiological condition of a collection of cells or of
an organism. This
term includes pre-clinical indicators.
"Clinical parameters" encompasses all non-sample or non-Precision ProfilesTM
of a
subject's health status or other characteristics, such as, without limitation,
age (AGE), ethnicity
(RACE), gender (SEX), and family history of lupus.
A "composition" includes a chemical compound, a nutraceutical, a
pharmaceutical, a
homeopathic formulation, an allopathic formulation, a naturopathic
formulation, a combination
of compounds, a toxin, a food, a food supplement, a mineral, and a complex
mixture of
substances, in any physical state or in a combination of physical states.
To "derive" a profile data set from a sample includes determining a set of
values
associated with constituents of a Gene Expression Panel (Precision ProfileTM)
either (i) by direct
measurement of such constituents in a biological sample. "Distinct RNA or
protein constituent"
in a panel of constituents is a distinct expressed product of a gene, whether
RNA or protein. An.
"expression" product of a gene includes the gene product whether RNA or
protein resulting from
translation of the messenger RNA.
"FN" is false negative, which for a disease state test means classifying a
disease subject
incorrectly as non-disease or normal.
"FP" is false positive, which for a disease state test means classifying a
normal subject
incorrectly as having disease.
A` formula," "algorithm," or "modeP' is any mathematical equation,
algorithmic,
analytical or programmed process, statistical technique, or comparison, that
takes one or more
continuous or categorical inputs (herein called "parameters") and calculates
an output value,
sometimes referred to as an "index" or "index value." Non-limiting examples of
"formulas"
include comparisons to reference values or profiles, sums, ratios, and
regression operators, such
as coefficients or exponents, value transformations and normalizations
(including, without
limitation, those normalization schemes based on clinical parameters, such as
gender, age, or
ethnicity), rules and guidelines, statistical classification models, and
neural networks trained on
historical populations. Of particular use in combining constituents of a Gene
Expression Panel
(Precision ProfileTM) are linear and non-linear equations and statistical
significance and
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classification analyses to determine the relationship between levels of
constituents of a Gene
Expression Panel (Precision ProfileTM) detected in a subject sample and the
subject's risk of
lupus. In panel and combination construction, of particular interest are
structural and synactic
statistical classification algorithms, and methods of risk index construction,
utilizing pattern
recognition features, including, without limitation, such established
techniques such as cross-
correlation, Principal Components Analysis (PCA), factor rotation, Logistic
Regression Analysis
(LogReg), Kolmogorov Smirnoff tests (KS), Linear Discriminant Analysis (LDA),
Eigengene
Linear Discriminant Analysis (ELDA), Support Vector Machines (SVM), Random
Forest (RF),
Recursive Partitioning Tree (RPART), as well as other related decision tree
classification
techniques (CART, LART, LARTree, FlexTree, amongst others), Shrunken Centroids
(SC),
StepAIC, K-means, Kth-Nearest Neighbor, Boosting, Decision Trees, Neural
Networks,
Bayesian Networks, Support Vector Machines, and Hidden Markov Models, among
others.
Other techniques may be used in survival and time to event hazard analysis,
including Cox,
Weibull, Kaplan-Meier and Greenwood models well known to those of skill in the
art. Many of
these techniques are useful either combined with a consituentes of a Gene
Expression Panel
(Precision ProfileTM) selection technique, such as forward selection,
backwards selection, or
stepwise selection, complete enumeration of all potential panels of a given
size, genetic
algorithms, voting and committee methods, or they may themselves include
biomarker selection
methodologies in their own technique. These may be coupled with information
criteria, such as
Akaike's Information Criterion (AIC) or Bayes Information Criterion (BIC), in
order to quantify
the tradeoff between additional biomarkers and model improvement, and to aid
in minimiziiig
overfit. The resulting predictive models may be validated in other clinical
studies, or cross-
validated within the study they were originally trained in, using such
techniques as Bootstrap,
Leave-One-Out (LOO) and 10-Fold cross-validation (10-Fold CV). At various
steps, false
discovery rates (FDR) may be estimated by value permutation according to
techniques known in
the art.
A "Gene Expression PaneP' (Precision ProfileT) is an experimentally verified
set of
constituents, each constituent being a distinct expressed product of a gene,
whether RNA or
protein, wherein constituents of the set are selected so that their
measurement provides a
measurement of a targeted biological condition.
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A "Gene Expression Profile" (Precision ProfileTm) is a set of values
associated with
constituents of a Gene Expression Panel resulting from evaluation of a
biological sample (or
population or set of samples).
A "Gene Expression Profile Inflammation Index" is the value of an index
function that
provides a mapping from an instance of a Gene Expression Profile into a single-
valued measure
of inflammatory condition.
A Gene Expression Profile Lupus Index" is the value of an index function that
provides a
mapping from an instance of a Gene Expression Profile into a single-valued
measure of a lupus
condition.
The "health" of a subject includes mental, emotional, physical, spiritual,
allopathic,
naturopathic and homeopathic condition of the subject.
"Index" is an arithmetically or mathematically derived numerical
characteristic developed
for aid in simplifying or disclosing or informing the analysis of more complex
quantitative
information. A disease or population index may be determined by the
application of a specific
algorithm to a plurality of subjects or samples with a conunon biological
condition.
"Inflammation" is used herein in the general medical sense of the word and may
be an
acute or chronic; simple or suppurative; localized or disseminated; cellular
and tissue response
initiated or sustained by any number of chemical, physical or biological
agents or combination of
agents.
"Inflammatory state" is used to indicate the relative biological condition of
a subject
resulting from inflammation, or characterizing the degree of inflammation.
A "large number" of data sets based on a common panel of genes is a number of
data sets
sufficiently large to permit a statistically significant conclusion to be
drawn with respect to an
instance of a data set based on the same panel.
The term "lupus" is used to indicate a chronic inflammatory disease that can
affect
various parts of the body, especially the skin, joints, blood, and kidneys. As
defined herein, the
term lupus encompasses systemic lupus erythematosus, cutaneous lupus
erythematosus
(including acute, subacute, and discoid lupus erythematosus), lupus
erythematosus tumidus,
hypertrophic variant, drug induced lupus, and neonatal lupus.
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The term "lupus treatment"encompasses both a composition or other agent for
the
amelioration of the disease and/or symptoms of lupus, and stimulus for the
induction of the
disease and/or symptoms of lupus.
"Negative predictive value" or "NPV" is calculated by TN/(TN + FN) or the true
negative fraction of all negative test results. It also is inherently impacted
by the prevalence of
the disease and pre-test probability of the population intended to be tested.
See, e.g., O'Marcaigh AS, Jacobson RM, "Estimating the Predictive Value of a
Diagnostic Test, How to Prevent Misleading or Confusing Results," Clin. Ped.
1993, 32(8): 485-
491, which discusses specificity, sensitivity, and positive and negative
predictive values of a test,
e.g., a clinical diagnostic test. Often, for binary disease state
classification approaches using a
continuous diagnostic test measurement, the sensitivity and specificity is
summarized by
Receiver Operating Characteristics (ROC) curves according to Pepe et al.,
"Limitations of the
Odds Ratio in Gauging the Performance of a Diagnostic, Prognostic, or
Screening Marker," Am.
J. Epidemiol 2004, 159 (9): 882-890, and summarized by the Area Under the
Curve (AUC) or c-
statistic, an indicator that allows representation of the sensitivity and
specificity of a test, assay,
or method over the entire range of test (or assay) cut points with just a
single value. See also,
e.g., Shultz, "Clinical Interpretation of Laboratory Procedures," chapter 14
in Teitz,
Fundamentals of Clinical Chemistry, Burtis and Ashwood (eds.), 4th edition
1996, W.B.
Saunders Company, pages 192-199; and Zweig et al., "ROC Curve Analysis: An
Example
Showing the Relationships Among Serum Lipid and Apolipoprotein Concentrations
in
Identifying Subjects with Coronory Artery Disease," Clin. Chem., 1992, 38(8):
1425-1428. An
alternative approach using likelihood functions, BIC, odds ratios, information
theory, predictive
values, calibration (including goodness-of-fit), and reclassification
measurements is summarized
according to Cook, "Use and Misuse of the Receiver Operating Characteristic
Curve in Risk
Prediction," Circulation 2007, 115: 928-935.
A"normal" subject is a subject who is generally in good health, has not been
diagnosed
with lupus, or one who is not suffering from lupus, is asymptomatic for lupus,
and lacks the
traditional laboratory risk factors for lupus.
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A "normative" condition of a subject to whom a composition is to be
administered means
the condition of a subject before administration, even if the subject happens
to be suffering from
a disease.
A "panel" of genes is a set of genes including at least two constituents.
A "population of cells" refers to any group of cells wherein there is an
underlying
commonality or relationship between the members in the population of cells,
including a group
of cells taken from an organism or from a culture of cells or from a biopsy,
for example.
"Positive predictive value" or "PPV" is calculated by TP/(TP+FP) or the true
positive
fraction of all positive test results. It is inherently impacted by the
prevalence of the disease and
1o pre-test probability of the population intended to be tested.
"Risk" in the context of the present invention, relates to the probability
that an event will
occur over a specific time period, and can mean a subject's "absolute" risk or
"relative" risk.
Absolute risk can be measured with reference to either actual observation post-
measurement for
the relevant time cohort, or with reference to index values developed from
statistically valid
historical cohorts that have been followed for the relevant time period.
Relative risk refers to the
ratio of absolute risks of a subject compared either to the absolute risks of
lower risk cohorts,
across population divisions (such as tertiles, quartiles, quintiles, or
deciles, etc.) or an average
population risk, which can vary by how clinical risk factors are assessed.
Odds ratios, the
proportion of positive events to negative events for a given test result, are
also commonly used
(odds are according to the formula p/(l-p) where p is the probability of event
and (1- p) is the
probability of no event) to no-conversion.
"Risk evaluation," or."evaluation of risk" in the context of the present
invention
encompasses making a prediction of the probability, odds, or likelihood that
an event or disease
state may occur, and/or the rate of occurrence of the event or conversion from
one disease state
to another, i.e., from a normal condition to lupus and vice versa. Risk
evaluation can also
comprise prediction of future clinical parameters, traditional laboratory risk
factor values, or
other indices of lupus results, either in absolute or relative terms in
reference to a previously
measured population. Such differing use may require different consituentes of
a Gene
Expression Panel (Precision ProfileTM) combinations and individualized panels,
mathematical
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algorithms, and/or cut-off points, but be subject to the same aforementioned
measurements of
accuracy and performance for the respective intended use.
A "sample" from a subject may include a single cell or multiple cells or
fragments of
cells or an aliquot of body fluid, taken from the subject, by means including
venipuncture,
excretion, ejaculation, massage, biopsy, needle aspirate, lavage sample,
scraping, surgical
incision or intervention or other means known in the art. The sample is blood,
urine, spinal fluid,
lymph, mucosal secretions, prostatic fluid, semen, haemolymph or any other
body fluid known in
the art for a subject. The sample is also a tissue sample.
"Sensitivity" is calculated by TP/(TP+FN) or the true positive fraction of
disease subjects.
"Specificity" is calculated by TN/(TN+FP) or the true negative fraction of non-
disease or
normal subjects.
By "statistically significant", it is meant that the alteration is greater
than what might be
expected to happen by chance alone (which could be a "false positive").
Statistical significance
can be determined by any method known in the art. Commonly used measures of
significance
include the p-value, which presents the probability of obtaining a result at
least as extreme as a
given data point, assuming the data point was the result of chance alone. A
result is often
considered highly significant at a p-value of 0.05 or less and statistically
significant at a p-value
of 0.10 or less. Such p-values depend significantly on the power of the study
performed.
A "set" or "population" of samples or subjects refers to a defined or selected
group of
samples or subjects wherein there is an underlying commonality or relationship
between the
members included in the set or population of samples or subjects.
A "Signature Profile" is an experimentally verified subset of a Gene
Expression Profile
selected to discriminate a biological condition, agent or physiological
mechanism of action.
A "Signature Panel" is a subset of a Gene Expression Panel (Precision
Profilethe
constituents of which are selected to permit discrimination of a biological
condition, agent or
physiological mechanism of action.
A "subject" is a cell, tissue, or organism, human or non-human, whether in
vivo, ex vivo
or in vitro, under observation. As used herein, reference to evaluating the
biological condition of
a subject based on a sample from the subject, includes using blood or other
tissue sample from a
human subject to evaluate the human subject's condition; it also includes, for
example, using a
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blood sample itself as the subject to evaluate, for example, the effect of
therapy or an agent upon
the sample.
A "stimulus" includes (i) a monitored physical interaction with a subject, for
example
ultraviolet A or B, or light therapy for seasonal affective disorder, or
treatment of psoriasis with
psoralen or treatment of cancer with embedded radioactive seeds, other
radiation exposure, and
(ii) any monitored physical, mental, emotional, or spiritual activity or
inactivity of a subject.
"Therapy" includes all interventions whether biological, chemical, physical,
metaphysical, or combination of the foregoing, intended to sustain or alter
the monitored
biological condition of a subject.
"TN" is true negative, which for a disease state test means classifying a non-
disease or
normal subject correctly.
"TP" is true positive, which for a disease state test means correctly
classifying a disease
subject.
The PCT patent application publication number WO 01/25473, published April 12,
2001,
entitled "Systems and Methods for Characterizing a Biological Condition or
Agent Using
Calibrated Gene Expression Profiles," which is herein incorporated by
reference, discloses the
use of Gene Expression Panels (Precision Profiles'. ) for the evaluation of
(i) biological condition
(including with respect to health and disease) and (ii) the effect of one or
more agents on
biological condition (including with respect to health, toxicity, therapeutic
treatment and drug
interaction).
In particular, the Gene Expression Panels (Precision ProfilesT) described
herein may be
used, without limitation, for measurement of the following: therapeutic
efficacy of natural or
synthetic compositions or stimuli that may be formulated individually or in
combinations or
mixtures for a range of targeted biological conditions; prediction of
toxicological effects and
dose effectiveness of a composition or mixture of compositions for an
individual or for a
population or set of individuals or for a population of cells; determination
of how two or more
different agents administered in a single treatment might interact so as to
detect any of
synergistic, additive, negative, neutral or toxic activity; performing pre-
clinical and clinical trials
by providing new criteria for pre-selecting subjects according to informative
profile data sets for
revealing disease status; and conducting preliminary dosage studies for these
patients prior to
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conducting phase 1 or 2 trials. These Gene Expression Panels (Precision
Profiles) may be
employed with respect to samples derived from subjects in order to evaluate
their biological
condition.
The present invention provides Gene Expression Panels (Precision ProfilesT)
for the
evaluation or characterization of lupus and conditions related to lupus in a
subject. In addition,
the Gene Expression Panels described herein also provide for the evaluation of
the effect of one
or more agents for the treatment of lupus and conditions related to lupus.
The Gene Expression Panels (Precision Profiles') are referred to herein as the
"Precision
ProfileTm for Lupus" and the "Precision ProfileTm for Inflammatory Response".
A Precision
ProfileTm for Lupus includes one or more genes, e.g., constituents, listed in
Tables 1-7, 9-13, and
15-20, whose expression is associated with lupus or conditions related to
lupus. A Precision
ProfileTm for Inflammatory Response includes one or more genes, e.g.,
constituents, listed in
Table 2, whose expression is associated with inflammatory response and lupus.
Each gene of the
Precision ProfileTm for Lupus and Precision ProfileTm for Inflammatory
Response is refered to
herein as a lupus associated gene or a lupus associated constituent.
It has been discovered that valuable and unexpected results may be achieved
when the
quantitative measurement of constituents is performed under repeatable
conditions (within a
degree of repeatability of measurement of better than twenty percent,
preferably ten percent or
better, more preferably five percent or better, and more preferably three
percent or better). For
the purposes of this description and the following claims, a degree of
repeatability of
measurement of better than twenty percent may be used as providing measurement
conditions
that are "substantially repeatable". In particular, it is desirable that each
time a measurement is
obtained corresponding to the level of expression of a constituent in a
particular sample,
substantially the same measurement should result for substantially the same
level of expression.
In this manner, expression levels for a constituent in a Gene Expression Panel
(Precision
ProfileTM) may be meaningfully compared from sample to sample. Even if the
expression level
measurements for a particular constituent are inaccurate (for example, say,
30% too low), the
criterion of repeatability means that all measurements for this constituent,
if skewed, will
nevertheless be skewed systematically, and therefore measurements of
expression level of the
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constituent may be compared meaningfully. In this fashion valuable information
may be
obtained and compared concerning expression of the constituent under varied
circumstances.
In addition to the criterion of repeatability, it is desirable that a second
criterion also be
satisfied, namely that quantitative measurement of constituents is performed
under conditions
wherein efficiencies of amplification for all constituents are substantially
similar as defined
herein. When both of these criteria are satisfied, then measurement of the
expression level of
one constituent may be meaningfully compared with measurement of the
expression level of
another constituent in a given sample and from sample to sample.
The evaluation or characterization of lupus is defined to be diagnosing lupus,
assessing
the presence or absence of lupus, assessing the risk of developing lupus, or
assessing the
prognosis of a subject with lupus. Similarly, the evaluation or
characterization of an agent for
treatment of lupus includes identifying agents suitable for the.treatment of
lupus. The agents can
be compounds known to treat lupus or compounds that have not been shown to
treat lupus.
Lupus and conditions related to lupus is evaluated by determining the level of
expression
(e.g., a quantitative measure) of an effective number (e.g., one or more) of
constituents of a Gene
Expression Panel (Precision Profile"") disclosed herein (i.e., Tables 1-2). By
an effective number
is meant the number of constituents that need to be measured in order to
discriminate between a
normal subject and a subject having lupus. Preferably the constituents are
selected as to
discriminate between a normal subject and a subject having lupus with at least
75% accuracy,
more preferably 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater accuracy.
The level of expression is determined by any means known in the art, such as
for
example quantitative PCR. The measurement is obtained under conditions that
are substantially
repeatable. Optionally, the qualitative measure of the constituent is compared
to a reference or
baseline level or value (e.g. a baseline profile set). In one embodiment, the
reference or baseline
level is a level of expression of one or more constituents in one or more
subjects known not to be
suffering from lupus (e.g., normal, healthy individual(s)). Alternatively, the
reference or
baseline level is derived from the level of expression of one or more
constituents in one or more
subjects known to be suffering from lupus. Optionally, the baseline level is
derived from the
same subject from which the first measure is derived. For example, the
baseline is taken from a
subject prior to receiving treatment or surgery for lupus, or at different
time periods during a
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course of treatment. Such methods allow for the evaluation of a particular
treatment for a
selected individual. Comparison can be performed on test (e.g., patient) and
reference samples
(e.g., baseline) measured concurrently or at temporally distinct times. An
example of the latter is
the use of compiled expression information, e.g., a gene expression database,
which assembles
information about expression levels of lupus associated genes.
A reference or baseline level or value as used herein can be used
interchangeably and is
meant to be relative to a number or value derived from population studes,
including without
limitation, such subjects having similar age range, subjects in the same or
similar ethnic group,
sex, or, in female subjets, pre-menopausal or post-menopausal subjects, or
relative to the starting
sample of a subject undergoing treatment for lupus. Such reference values can
be derived from
statistical analyses and/or risk prediction data of populations obtained from
mathematical
algorithms and computed indices of lupus. Reference indices can also be
constructed and used
using algoriths and other methods of statistical and structural
classification.
In one embodiment of the present invention, the reference or baseline value is
the amount
of expression of a lupus associated gene in a control sample derived from one
or more subjects
who are both asymptomatic and lack traditional laboratory risk factors for
lupus.
In another embodiment of the present invention, the reference or baseline
value is the
level of lupus associated genes in a control sample derived from one or more
subjects who are
not at risk or at low risk for developing lupus.
In a further embodiment, such subjects are monitored and/or periodically
retested for a
diagnostically relevant period of time ("longitudinal studies") following such
test to verify
continued absence from lupus. Such period of time may be one year, two years,
two to five
years, five years, five to ten years, ten years, or ten or more years from the
initial testing date for
determination of the reference or baseline value. Furthermore, retrospective
measurement of
lupus associated genes in properly banked historical subject samples may be
used in establishing
these reference or baseline values, thus shortening the study time required,
presuming the
subjects have been appropriately followed during the intervening period
through the intended
horizon of the product claim.
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A reference or basline value can also comprise the amounts of lupus associated
genes
derived from subjects who show an improvement in lupus status as a result of
treatments and/or
therapies for the lupus being treated and/or evaluated.
In another embodiment, the reference or baseline value is an index value or a
baseline
value. An index value or baseline value is a composite sample of an effective
amount of lupus
associated genes from one or more subjects who do not have lupus.
For example, where the reference or baseline level is comprised of the amounts
of lupus
associated genes derived from one or more subjects who have not been diagnosed
with lupus or
are not known to be suffereing from lupus, a change (e.g., increase or
decrease) in the expression
level of a lupus associated gene in the patient-derived sample of a lupus
associated gene
compared to the expression level of such gene in the reference or baseline
level indicates that the
subject is suffering from or is at risk of developing lupus. In contrast, when
the methods are
applied prophylacticly, a similar level of expression in the patient-derived
sample of a lupus
associated gene as compared to such gene in the baseline level indicates that
the subject is:not
suffering from or at risk of developing lupus.
Where the reference or baseline level is comprised of the amounts of lupus
associated
genes derived from one or more subjects who have been diagnosed with lupus, or
are known to
be suffereing from lupus, a similarity in the expression pattern in the
patient-derived sample of a
lupus associated gene compared to the lupus baseline level indicates that the
subject is suffering
from or is at risk of developing lupus.
Expression of a lupus associated gene also allows for the course of treatment
of lupus to
be monitored. In this method, a biological sample is provided from a subject
undergoing
treatment, e.g., if desired, biological samples are obtained from the subject
at various time points
before, during, or after treatment. Expression of a lupus associated gene is
then determined and
compared to a reference or baseline profile. The baseline profile may be taken
or derived from
one or more individuals who have been exposed to the treatment. Alternatively,
the baseline
level may be taken or derived from one or more individuals who have not been
exposed to the
treatment. For example, samples may be collected from subjects who have
received initial
treatment for lupus and subsequent treatment for lupus to monitor the progress
of the treatment.
Differences in the genetic makeup of individuals can result in differences in
their relative
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abilities to metabolize various drugs. Accordingly, the Precision Profile`m
for Lupus (Table 1)
and the Precision Profile'm for Inflammatory Response (Table 2) disclosed
herein allow for a
putative therapeutic or prophylactic to be tested from a selected subject in
order to determine if
the agent is a suitable for treating or preventing lupus in the subject.
Additionally, other genes
known to be associated with toxicity may be used. By suitable for treatment is
meant
determining whether the agent will be efficacious, not efficacious, or toxic
for a particular
individual. By toxic it is meant that the manifestations of one or more
adverse effects of a drug
when administered therapeutically. For example, a drug is toxic when it
disrupts one or more
normal physiological pathways.
To identify a therapeutic that is appropriate for a specific subject, a test
sample from the
subject is exposed to a candidate therapeutic agent, and the expression of one
or more of lupus
genes is determined. A subject sample is incubated in the presence of a
candidate agent and the
pattern of lupus associated gene expression in the test sample is measured and
compared to a
baseline profile, e.g., a lupus baseline profile or a non-lupus baseline
profile or an index value.
The test agent can be any compound or composition. For example, the test agent
is a compound
known to be useful in the treatment of lupus. Alternatively, the test agent is
a compound that has
not previously been used to treat lupus.
If the reference sample, e.g., baseline is from a subject that does not have
lupus a
similarity in the pattern of expression of lupus genes in the test sample
compared to the reference
sample indicates that the treatment is efficacious. Whereas a change in the
pattern of expression
of lupus genes in the test sample compared to the reference sample indicates a
less favorable
clinical outcome or prognosis. By "efficacious" is meant that the treatment
leads to a decrease of
a sign or symptom of lupus in the subject or a change in the pattern of
expression of a lupus
associated gene such that the gene expression pattern has an increase in
similarity to that of a
reference or baseline pattern. Assessment of lupus is made using standard
clinical protocols.
Efficacy is determined in association with any known method for diagnosing or
treating lupus.
A Gene Expression Panel (Precision ProfileT`) is selected in a manner so that
quantitative
measurement of RNA or protein constituents in the Panel constitutes a
measurement of a
biological condition of a subject. In one kind of arrangement, a calibrated
profile data set is
employed. Each member of the calibrated profile data set is a function of (i)
a measure of a
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distinct constituent of a Gene Expression Panel (Precision ProfileTe') and
(ii) a baseline quantity.
Additional embodiments relate to the use of an index or algorithm resulting
from
quantitative measurement of constituents, and optionally in addition, derived
from either expert
analysis or computational biology (a) in the analysis of complex data sets;
(b) to control or
normalize the influence of uninformative or otherwise minor variances in gene
expression values
between samples or subjects; (c) to simplify the characterization of a complex
data set for
comparison to other complex data sets, databases or indices or algorithms
derived from complex
data sets; (d) to monitor a biological condition of a subject; (e) for
measurement of therapeutic
efficacy of natural or synthetic compositions or stimuli that may be
formulated individually or in
l0 combinations or mixtures for a range of targeted biological conditions; (f)
for predictions of
toxicological effects and dose effectiveness of a composition or mixture of
compositions for an
individual or for a population or set of individuals or for a population of
cells; (g) for
determination of how two or more different agents administered in a single
treatment might
interact so as to detect any of synergistic, additive, negative, neutral of
toxic activity (h) for
performing pre-clinical and clinical trials by providing new criteria for pre-
selecting subjects
according to informative profile data sets for revealing disease status and
conducting preliminary
dosage studies for these patients prior to conducting Phase 1 or 2 trials.
Gene expression profiling and the use of index characterization for a
particular condition
or agent or both may be used to reduce the cost of Phase 3 clinical trials and
may be used beyond
Phase 3 trials; labeling for approved drugs; selection of suitable medication
in a class of
medications for a particular patient that is directed to their unique
physiology; diagnosing or
determining a prognosis of a medical condition or an infection which may
precede onset of
symptoms or alternatively diagnosing adverse side effects associated with
administration of a
therapeutic agent; managing the health care of a patient; and quality control
for different batches
of an agent or a mixture of agents.
The subject
The methods disclosed here may be applied to cells of humans, mammals or other
organisms without the need for undue experimentation by one of ordinary skill
in the art because
all cells transcribe RNA and it is known in the art how to extract RNA from
all types of cells.
A subject can include those who have not been previously diagnosed as having
lupus or a
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condition related to lupus. Alternatively, a subject can also include those
who have already been
diagnosed as having lupus or a condition related to lupus. Diagnosis of
systemic lupus is made,
for example, from any one or combination of the following procedures and
symptoms: 1) a
physical exam; 2) blood tests. Usually a diagnosis can be made when there is
evidence of a
number of the main warning signs of SLE, and other conditions that can also
indicate the
presence of SLE, such as: 1) pleuritis, an inflammation of the lining of the
lungs, or pericarditis,
an inflammation of the lining of the heart; 2) decreased kidney function,
which may be mild or
severe; 3) central nervous system involvement (may be exhibited by seizures or
psychosis); 4)
decreased blood cell count (red blood cells, white blood cells, or platelets);
5) autoantibodies
present in the blood; or 6) antinuclear antibodies present in the blood; 7)
worsening of
inflammation (e.g., lesions, rash, joint pain) after sun exposure.
Diagnosis of cutaneous lupus can be made from a skin biopsy, alone or in
combination
with serological testing, e.g., anti-nuclear antibody test or anti-Ro test. .
Optionally, the subject has previously been treated with a therapeutic agent,
including but
not limited to therapeutic agents for the treatment of systemic or cutaneous
lupus, such as
acetaminophen (to manage pain), non-steroidal anti-inflammatory drugs (NSAIDs,
to manage
pain and inflammation), oral cortisone (e.g., prednisone to reduce
inflammation), antimalarial
medications (e.g., Aralen (chloroquine) and Plaquenil (hydroxychloroquine)to
manage fatigue,
skin rashes and joint pain); and cytotoxic drugs (e.g., azathioprine,
acitretin, thalidomide,
cyclosporine gold, methotrexate, intravenous immunoglobulin, clofazamine,
dapsone, and
cyclophosphamide to control inflammation and the immune system).
A subject can also include those who are suffering from, or at risk of
developing lupus or
a condition related to lupus, such as those who exhibit known risk factors for
lupus or conditions
related to lupus. For example, known risk factors for lupus include but are
not limited to: gender
(women between the ages of 20 and 50); ethnicity (African Americans,
Hispanics, and Asians
are more susceptible to the disease); family history (an immediate family
member of a lupus
patient has 20 times the risk as someone without an immediate family member);
and long-term
use of certain drugs such as glyburide, calcium channel blockers (diltiazem,
felodipine),
hydrochlorothiazide, angiotensin-converting-enzyme inhibitors, and
penicillamine.
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Selecting Constituents of a Gene Expression Panel (Precision Profile"M)
The general approach to selecting constituents of a Gene Expression Panel
(Precision
Profile'*') has been described in PCT application publication number WO
01/25473, incorporated
herein by reference in its entirety. A wide range of Gene Expression Panels
(Precision ProfilesT`d)
have been designed and experimentally validated, each panel providing a
quantitative measure of
biological condition that is derived from a sample of blood or other tissue.
For each panel,
experiments have verified that a Gene Expression Profile using the panel's
constituents is
informative of a biological condition. (It has also been demonstrated that in
being informative of
biological condition, the Gene Expression Profile is used, among other things,
to measure the
effectiveness of therapy, as well as to provide a target for therapeutic
intervention.).
Inflammation and Lupus
Tables 1-7, 9-13, and 15-201isted below, include relevant genes which may be
selected
for a given Precision Profiles'T', such as the Precision Profiles'm
demonstrated herein to be useful
in the evaluation of lupus and conditions related to lupus. The Precision
ProfileTM for Lupus
(Table 1) is a panel of 134 genes, whose expression is associated with lupus
or conditions related
to lupus.
In addition to the Precision ProfileTM for Lupus (Table 1), the Precision
ProfileTM for
Inflammatory Response (Table 2) include relevant genes which may be selected
for a given
Precision Profiles'm, such as the Precision Profiles'T' demonstrated herein to
be useful in the
evaluation of lupus and conditions related to lupus.
The Precision ProfileTM for Inflammatory Response (Table 2) is a panel of
genes whose
expression is associated with inflammatory response. The disease lupus
involves chronic
inflammation that can effect many parts of the body, including the heart,
lung, skin, joints, blood
forming organs, kidneys, and nervous system. As such, both the lupus genes
listed in Table 1 and
the inflammatory response genes listed in Table 2 can be used to detect lupus
and distinguish
between subjects suffering from lupus and normal subjects.
Gene Expression Profiles Based on Gene Expression Panels of the Present
Invention
Tables 6-8 were derived from a study of the gene expression patterns described
in
Example 1 below. Tables 6-8 describe a 2-gene model, LGALS3BP and SGK, based
on genes
from the Precision Profile"m for Lupus (shown in Table 1), derived from latent
class modeling of
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the subjects from this study using 1 and 2 gene models to distinguish between
subjects suffering
from discoid lupus (DLE), subacute cutaneous lupus (SCLE), lupus tumidus
(LET), and Source
1VIDx normal subjects (Normals). This two-gene model is capable of correctly
classifying the
lupus-afflicted and Normal subjects with at least 75% accuracy. For example,
in Table 8, it can
be seen that the 2-gene model, LGALS3BP and SGK correctly classifies Normal
subjects with
97% accuracy, DLE afflicted subjects with 81% accuracy, SCLE afflicted
subjects with 91%
accuracy.
Tables 13-14 were derived from a study of the gene expression patterns
described in
Example 2 below. Tables 13-14 describe the 2-gene model OASL and THBS1, based
on genes
from the Precision Profile"m for Lupus (shown in Table 1), derived from latent
class modeling of
the subjects from this study using 1 and 2 gene models to distinguish between
subjects suffering
from discoid lupus (DLE), subacute cutaneous lupus (SCLE), and Source 1VIDx
normal subjects
(Normals). This two-gene model is capable of correctly classifying the lupus-
afflicted: and
Normal subjects with at least 75% accuracy. For example, in Table 14, it can
be seen that the 2-
gene model, OASL and THBS1 correctly classifies Normal subjects with 98%
accuracy, DLE
afflicted subjects with 88% accuracy, and SCLE afflicted subjects with 91%
accuracy.
Tables 17-20 are derived from a study of the gene expression patterns
described in
Example 3 below. Tables 17 and 18 each describe a multitude of 2-gene and 3-
gene models,
respectively, based on genes from the Precision Profile for Lupus (shown in
Table 1), derived
from latent class modeling of the subjects from this study using 1, 2 and 3-
gene models to
distinguish between DLE/SCLE-afflicted subjects and Source 1VIDx Normal
(Normal)lHealthy
Volunteer (HV) subjects. Constituent models selected from Tables 17 and 18 are
capable of
correctly classifying DLE/SCLE-afflicted subjects and Normal/ HV subjects with
at least 75%
accuracy. For example, as shown in Table 17, the two-gene model, SERPINGI and
FCGRIA, is
capable of classifying DLE/SCLE subjects with at least 96% accuracy, and
normal/HV subjects
with at least 95% accuracy. As shown in Table 18, the three-gene model,
PLSCRI, FCGR2B,
and TNFRSF5, is capable of classifying DLE/SCLE subjects with at least 96%
accuracy and
Normal/HV subjects with at least 98% accuracy.
Tables 19 and 20 each describe a multitide of 2-gene and 3-gene models,
respectively,
based on genes from the Precision Profile for Lupus (shown in Table 1),
derived from latent
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class modeling of the subjects from this study using 1, 2 and 3-gene models to
distinguish
between LET-afflicted subjects and Normal/HV subjects. Constituent models
selected from
Tables 19 and 20 are capable of correctly classifying LET-afflicted subjects
and Normal/HV
subjects with at least 75% accuracy. For example, as shown in Table 19, the
two-gene model,
LGALS3BP and CCR10, is capable of classifying LET-afflicted subjects with at
least 77%
accuracy, and Normal/HV subjects with at least 95% accuracy. As shown in Table
20, the three-
gene model, LGALS3BP, SGK, and THBS1, is capable of classifying LET-afflicted
subjects
with at least 77% accuracy, and Normal/HV subjects with at least 93% accuracy.
In general, panels may be constructed and experimentally validated by one of
ordinary
skill in the art in accordance with the principles articulated in the present
application.
Design of assays
Typically, a sample is run through a panel in replicates of three for each
target gene
(assay); that is, a sample is divided into aliquots and for each aliquot the
concentrations of each
constituent in a Gene Expression Panel (Precision ProfileT') is measured. From
over thousands
of constituent assays, with each assay conducted in triplicate, an average
coefficient of variation
was found (standard deviation/average)* 100, of less than 2 percent among the
normalized OCt
measurements for each assay (where normalized quantitation of the target mRNA
is determined
by the difference in threshold cycles between the internal control (e.g., an
endogenous marker
such as 18S rRNA, or an exogenous marker) and the gene of interest. This is a
measure called
"intra-assay variability". Assays have also been conducted on different
occasions using the same
sample material. This is a measure of "inter-assay variability". Preferably,
the average
coefficient of variation of intra- assay variability or inter-assay
variability is less than 20%, more
preferably less than 10%, more preferably less than 5%, more preferably less
than 4%, more
preferably less than 3%, more preferably less than 2%, and even more
preferably less than 1%.
It has been determined that it is valuable to use the quadruplicate or
triplicate test results
to identify and eliminate data points that are statistical "outliers"; such
data points are those that
differ by a percentage greater, for example, than 3% of the average of all
three or four values.
Moreover, if more than one data point in a set of three or four is excluded by
this procedure, then
all data for the relevant constituent is discarded.
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Measurement of Gene Expression for a Constituent in the Panel
For measuring the amount of a particular RNA in a sample, methods known to one
of
ordinary skill in the art were used to extract and quantify transcribed RNA
from a sample with
respect to a constituent of a Gene Expression Panel (Precision Profile"').
(See detailed protocols
below. Also see PCT application publication number WO 98/24935 herein
incorporated by
reference for RNA analysis protocols). Briefly, RNA is extracted from a sample
such as any
tissue, body fluid, cell or culture medium in which a population of cells of a
subject might be
growing. For example, cells may be lysed and RNA eluted in a suitable solution
in which to
conduct a DNAse reaction. Subsequent to RNA extraction, first strand synthesis
may be
perfonmed using a reverse transcriptase. Gene amplification, more specifically
quantitative PCR
assays, can then be conducted and the gene of interest calibrated against an
internal marker such
as 18S rRNA (Hirayama et al., Blood 92, 1998: 46-52). Any other endogenous
marker can be
used, such as 28S-25S rRNA and 5S rRNA. Samples are measured in multiple
replicates, for
example, 3 replicates. In an embodiment of the invention, quantitative PCR is
performed using
amplification, reporting agents and instruments such as those supplied
commercially by Applied
Biosystems (Foster City, CA). Given a defined efficiency of amplification of
target transcripts,
the point (e.g., cycle number) that signal from amplified target template is
detectable may be
directly related to the amount of specific message transcript in the measured
sample. Similarly,.
other quantifiable signals such as fluorescence, enzyme activity,
disintegrations per minute,
absorbance, etc., when correlated to a known concentration of target templates
(e.g., a reference
standard curve) or normalized to a standard with limited variability can be
used to quantify the
number of target templates in an unknown sample.
Although not limited to amplification methods, quantitative gene expression
techniques
may utilize amplification of the target transcript. Alternatively or in
combination with
amplification of the target transcript, quantitation of the reporter signal
for an internal marker
generated by the exponential increase of amplified product may also be used.
Amplification of
the target template may be accomplished by isothermic gene amplification
strategies or by gene
amplification by thermal cycling such as PCR.
It is desirable to obtain a definable and reproducible correlation between the
amplified
target or reporter signal, i.e., internal marker, and the concentration of
starting templates. It has
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WO 2008/091708 PCT/US2008/001070
been discovered that this objective can be achieved by careful attention to,
for example,
consistent primer-template ratios and a strict adherence to a narrow
permissible level of
experimental amplification efficiencies (for example 80.0 to 100% +/- 5%
relative efficiency,
typically 90.0 to 100% +/- 5% relative efficiency, more typically 95.0 to 100%
+/- 2 %, and most
typically 98 to 100% +/- 1 % relative efficiency). In determining gene
expression levels with
regard to a single Gene Expression Profile, it is necessary that all
constituents of the panels,
including endogenous controls, maintain similar amplification efficiencies, as
defined herein, to
permit accurate and precise relative measurements for each constituent.
Amplification
efficiencies are regarded as being "substantially similar", for the purposes
of this description and
the following claims, if they differ by no more than approximately 10%,
preferably by less than
approximately 5%, more preferably by less than approximately 3%, and more
preferably by less
than approximately 1%. Measurement conditions are regarded as being
"substantially
repeatable, for the purposes of this description and the following claims, if
they differ by no
more than approximately +/- 10% coefficient of variation (CV), preferably by
less than
approximately +/- 5% CV, more preferably +/- 2% CV. These constraints should
be observed
over the entire range of concentration levels to be measured associated with
the relevant
biological condition. While it is thus necessary for various embodiments
herein to satisfy criteria
that measurements are achieved under measurement conditions that are
substantially repeatable
and wherein specificity and efficiencies of amplification for all constituents
are substantially
similar, nevertheless, it is within the scope of the present invention as
claimed herein to achieve
such measurement conditions by adjusting assay results that do not satisfy
these criteria directly,
in such a manner as to compensate for errors, so that the criteria are
satisfied after suitable
adjustment of assay results.
In practice, tests are run to assure that these conditions are satisfied. For
example, the
design of all primer-probe sets are done in house, experimentation is
performed to determine
which set gives the best performance. Even though primer-probe design can be
enhanced using
computer techniques known in the art, and notwithstanding common practice, it
has been found
that experimental validation is still useful. Moreover, in the course of
experimental validation,
the selected primer-probe combination is associated with a set of features:
CA 02676559 2009-07-24
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The reverse primer should be complementary to the coding DNA strand. In one
embodiment, the primer should be located across an intron-exon junction, with
not more than
four bases of the three-prime end of the reverse primer complementary to the
proximal exon. (If
more than four bases are complementary, then it would tend to competitively
amplify genomic
DNA.)
In an embodiment of the invention, the primer probe set should amplify cDNA of
less
than 110 bases in length and should not amplify, or generate fluorescent
signal from, genomic
DNA or transcripts or cDNA from related but biologically irrelevant loci.
A suitable target of the selected primer probe is first strand cDNA, which in
one
embodiment may be prepared from whole blood as follows:
(a) Use of whole blood for ex vivo assessment of a biological condition
Human blood is obtained by venipuncture and prepared for assay. The aliquots
of
heparinized, whole blood are mixed with additional test therapeutic compounds
and held at 37 C
in an atmosphere of 5% COZ for 30 minutes. Cells are lysed and nucleic acids,
e.g., RNA, are
extracted by various standard means.
Nucleic acids, RNA and or DNA, are purified from cells, tissues or fluids of
the test
population of cells: RNA is preferentially obtained from the nucleic acid mix
using a variety of
standard procedures (or RNA Isolation Strategies, pp. 55-104, in RNA Methodolo
egis_A
laboratory guide for isolation and characterization, 2nd edition, 1998, Robert
E. Farrell, Jr., Ed.,
Academic Press), in the present using a filter-based RNA isolation system from
Ambion
(RNAqueous Tm, Phenol-free Total RNA Isolation Kit, Catalog #1912, version
9908; Austin,
Texas).
(b) Amplification strategies.
Specific RNAs are amplified using message specific primers or random primers.
The
specific primers are synthesized from data obtained from public databases
(e.g., Unigene,
National Center for Biotechnology Information, National Library of Medicine,
Bethesda, MD),
including information from genomic and cDNA libraries obtained from humans and
other
animals. Primers are chosen to preferentially amplify from specific RNAs
obtained from the test
or indicator samples (see, for example, RT PCR, Chapter 15 in RNA Methodolo
egis, A
Laboratory Guide for Isolation and Characterization, 2nd edition, 1998, Robert
E. Farrell, Jr.,
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WO 2008/091708 PCT/US2008/001070
Ed., Academic Press; or Chapter 22 pp.143-151, RNA Isolation and
Characterization Protocols,
Methods in Molecular Biology, Volume 86, 1998, R. Rapley and D. L. Manning
Eds., Human
Press, or Chapter 14 Statistical refinement of primer design parameters; or
Chapter 5, pp.55-72,
PCR Applications: protocols for functional genomics, M.A.Innis, D.H. Gelfand
and J.J. Sninsky,
Eds., 1999, Academic Press). Amplifications are carried out in either
isothermic conditions or
using a thermal cycler (for example, a ABI 9600 or 9700 or 7900 obtained from
Applied
Biosystems, Foster City, CA; see Nucleic acid detection methods, pp. 1-24, in
Molecular
Methods for Virus Detection, D.L.Wiedbrauk and D.H., Farkas, Eds., 1995,
Academic Press).
Amplified nucleic acids are detected using fluorescent-tagged detection
oligonucleotide probes
(see, for example, TaqmanTM PCR Reagent Kit, Protocol, part number 402823,
Revision A,
1996, Applied Biosystems, Foster City CA) that are identified and synthesized
from publicly
known databases as described for the amplification primers.
For example, without limitation, amplified cDNA is detected and quantified
using
detection systems such as the ABI Prism 7900 Sequence Detection System
(Applied
Biosystems (Foster City, CA)), the Cepheid SmartCycler and Cepheid GeneXpert
Systems, the
Fluidigm BioMark'm System, and the Roche LightCycler 480 Real-Time PCR
System.
Amounts of specific RNAs contained in the test sample can be related to the
relative quantity of
fluorescence observed (see for example, Advances in Quantitative PCR
Technology: 5' Nuclease
Assays, Y.S. Lie and C.J. Petropolus, Current Opinion in Biotechnology, 1998,
9:43-48, or
Rapid Thermal Cycling and PCR Kinetics, pp. 211-229, chapter 14 in PCR
applications:
protocols for functional genomics, M.A. Innis, D.H. Gelfand and J.J. Sninsky,
Eds., 1999,
Academic Press). Examples of the procedure used with several of the above-
mentioned
detection systems are described below. In some embodiments, these procedures
can be used for
both whole blood RNA and RNA extracted from cultured cells. In some
embodiments, any
tissue, body fluid, or cell(s) may be used for ex vivo assessment of a
biological condition affected
by an agent. Methods herein may also be applied using proteins where sensitive
quantitative
techniques, such as an Enzyme Linked ImmunoSorbent Assay (ELISA) or mass
spectroscopy,
are available and well-known in the art for measuring the amount of a protein
constituent (see
WO 98/24935 herein incorporated by reference).
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An example of a procedure for the synthesis of first strand cDNA for use in
PCR
amplification is as follows:
Materials
1. Applied Biosystems TAQMAN Reverse Transcription Reagents Kit (P/N 808-
0234). Kit Components: lOX TaqMan RT Buffer, 25 mM Magnesium chloride,
deoxyNTPs
mixture, Random Hexamers, RNase Inhibitor, MultiScribe Reverse Transcriptase
(50 U/mL) (2)
RNase / DNase free water (DEPC Treated Water from Ambion (P/N 9915G), or
equivalent).
Methods
1. Place RNase Inhibitor and MultiScribe Reverse Transcriptase on ice
immediately.
All other reagents can be thawed at room temperature and then placed on ice.
2. Remove RNA samples from -80 C freezer and thaw at room temperature and
then place immediately on ice.
3. Prepare the following cocktail of Reverse Transcriptase Reagents for each
100
mL RT reaction (for multiple samples, prepare extra cocktail to allow for
pipetting error):
1 reaction (mL) 11X, e.g. 10 samples ( L)
l OX RT Buffer 10.0 110.0
mM MgC12 22.0 242.0
dNTPs 20.0 220.0
Random Hexamers 5.0 55.0
20 RNAse Inhibitor 2.0 22.0
Reverse Transcriptase 2.5 27.5
Water 18.5 203.5
Total: 80.0 880.0 (80 L per sample)
4. Bring each RNA sample to a total volume of 20 L in a 1.5 mL
microcentrifuge
25 tube (for example, remove 10 L RNA and dilute to 20 L with RNase / DNase
free water, for
whole blood RNA use 20 L total RNA) and add 80 L RT reaction mix from step
5,2,3. Mix
by pipetting up and down.
5. Incubate sample at room temperature for 10 minutes.
6. Incubate sample at 37 C for 1 hour.
7. Incubate sample at 90 C for 10 minutes.
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8. Quick spin samples in microcentrifuge.
9. Place sample on ice if doing PCR immediately, otherwise store sample at -20
C
for future use.
10. PCR QC should be run on all RT samples using 18S and 0-actin.
Following the synthesis of first strand cDNA, one particular embodiment of the
approach
for amplification of first strand cDNA by PCR, followed by detection and
quantification of
constituents of a Gene Expression Panel (Precision ProfileTm ) is performed
using the ABI Prism
7900 Sequence Detection System as follows:
Materials
1. 20X Primer/Probe Mix for each gene of interest.
2. 20X Primer/Probe Mix for 18S endogenous control.
3. 2X Taqman Universal PCR Master Mix.
4. cDNA transcribed from RNA extracted from cells.
5. Applied Biosystems 96-Well Optical Reaction Plates.
6. Applied Biosystems Optical Caps, or optical-clear film.
7. Applied Biosystem Prism 7700 or 7900 Sequence Detector.
Methods
1. Make stocks of each Primer/Probe mix containing the Primer/Probe for the
gene
of interest, Primer/Probe for 18S endogenous control, and 2X PCR Master Mix as
follows.
Make sufficient excess to allow for pipetting error e.g., approximately 10%
excess. The
following example illustrates a typical set up for one gene with quadruplicate
samples testing
two conditions (2 plates).
IX (I well) ( L)
2X Master Mix 7.5
20X 18S Primer/Probe Mix 0.75
20X Gene of interest Primer/Probe Mix 0.75
Total 9.0
2. Make stocks of cDNA targets by diluting 95 L of cDNA into 2000 L of water.
The amount of cDNA is adjusted to give Ct values between 10 and 18, typically
between 12 and
16.
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WO 2008/091708 PCT/US2008/001070
3. Pipette 9 L of Primer/Probe mix into the appropriate wells of an Applied
Biosystems 384-Well Optical Reaction Plate.
4. Pipette lO L of cDNA stock solution into each well of the Applied
Biosystems
384-Well Optical Reaction Plate.
5. Seal the plate with Applied Biosystems Optical Caps, or optical-clear film.
6. Analyze the plate on the ABI Prism 7900 Sequence Detector.
In another embodiment of the invention, the use of the primer probe with the
first strand
cDNA as described above to permit measurement of constituents of a Gene
Expression Panel
(Precision Profile'T') is performed using a QPCR assay on Cepheid SmartCycler
and
GeneXpert Instruments as follows:
I. To run a QPCR assay in duplicate on the Cepheid SmartCycler instrument
containing three
target genes and one reference gene, the following procedure should be
followed.
A. With 20X Primer/Probe Stocks.
Materials
1. SmartMixTM-HM lyophilized Master Mix.
2. Molecular grade water.
3. 20X Primer/Probe Mix for the 18S endogenous control gene. The endogenous
control gene will be dual labeled with VIC-MGB or equivalent.
4. 20X Primer/Probe Mix for each for target gene one, dual labeled with FAM-
BHQ1 or
equivalent.
5. 20X Primer/Probe Mix for each for target gene two, dual labeled with Texas
Red-
BHQ2 or equivalent.
6. 20X Primer/Probe Mix for each for target gene three, dual labeled with
Alexa 647-
BHQ3 or equivalent.
7. Tris buffer, pH 9.0
8. cDNA transcribed from RNA extracted from sample.
9. SmartCycler 25 L tube.
10. Cepheid SmartCycler instrument.
Methods
1. For each cDNA sample to be investigated, add the following to a sterile 650
L tube.
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SmartMixTM-HM lyophilized Master Mix 1 bead
20X 18S Primer/Probe Mix 2.5 L
20X Target Gene 1 Primer/Probe Mix 2.5 L
20X Target Gene 2 Primer/Probe Mix 2.5 L
20X Target Gene 3 Primer/Probe Mix 2.5 L
Tris Buffer, pH 9.0 2.5 L
Sterile Water 34.5 L
Total 47 L
Vortex the mixture for 1 second three times to completely mix the reagents.
Briefly
centrifuge the tube after vortexing.
2. Dilute the cDNA sample so that a 3 L addition to the reagent mixture above
will
give an 18S reference gene CT value between 12 and 16.
3. Add 3 L of the prepared cDNA sample to the reagent mixture bringing the
total
volume to 50 L. Vortex the mixture for 1 second three times to completely mix
the
reagents. Briefly centrifuge the tube after vortexing.
4. Add 25 L of the mixture to each of two SmartCycler tubes, cap the tube
and spin
for 5 seconds in a microcentrifuge having an adapter for SmartCycler tubes.
5. Remove the two SmartCycler tubes from the microcentrifuge and inspect for
air
bubbles. If bubbles are present, re-spin, otherwise, load the tubes into the
SmartCycler instrument.
6. Run the appropriate QPCR protocol on the SmartCycler , export the data and
analyze
the results.
B. With Lyophilized SmartBeadsTM.
Materials
1. SmartMixTM-HM lyophilized Master Mix.
2. Molecular grade water.
3. SmartBeadsTM containing the 18S endogenous control gene dual labeled with
VIC-
MGB or equivalent, and the three target genes, one dual labeled with FAM-BHQ1
or
equivalent, one dual labeled with Texas Red-BHQ2 or equivalent and one dual
labeled with Alexa 647-BHQ3 or equivalent.
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4. Tris buffer, pH 9.0
5. cDNA transcribed from RNA extracted from sample.
6. SmartCycler 25 L tube.
7. Cepheid SmartCycler instrument.
Methods
1. For each cDNA sample to be investigated, add the following to a sterile 650
L tube.
SmartMix-'. -HM lyophilized Master Mix 1 bead
SmartBead'T' containing four primer/probe sets 1 bead
Tris Buffer, pH 9.0 2.5 L
Sterile Water 44.5 L
Total 47 L
Vortex the mixture for 1 second three times to completely mix the reagents.
Briefly
centrifuge the tube after vortexing.
2. Dilute the cDNA sample so that a 3 L addition to the reagent mixture above
will
give an 18S reference gene CT value between 12 and 16.
3. Add 3 L of the prepared cDNA sample to the reagent mixture bringing the
total
volume to 50 L. Vortex the mixture for 1 second three times to completely mix
the
reagents. Briefly centrifuge the tube after vortexing.
4. Add 25 L of the mixture to each of two SmartCycler tubes, cap the tube
and spin
for 5 seconds in a microcentrifuge having an adapter for SmartCycler tubes.
5. Remove the two SmartCycler tubes from the microcentrifuge and inspect for
air
bubbles. If bubbles are present, re-spin, otherwise, load the tubes into the
SmartCycler instrument.
6. Run the appropriate QPCR protocol on the SmartCycler , export the data and
analyze
the results.
H. To run a QPCR assay on the Cepheid GeneXpert instrument containing three
target genes
and one reference gene, the following procedure should be followed. Note that
to do
duplicates, two self contained cartridges need to be loaded and run on the
GeneXpert
instrument.
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Materials
1. Cepheid GeneXpert self contained cartridge preloaded with a lyophilized
SmartMixn"-HM master mix bead and a lyophilized SmartBeadTM containing four
primer/probe sets.
2. Molecular grade water, containing Tris buffer, pH 9Ø
3. Extraction and purification reagents.
4. Clinical sample (whole blood, RNA, etc.)
5. Cepheid GeneXpert instrument.
Methods
1. Remove appropriate GeneXpert self contained cartridge from packaging.
2. Fill appropriate chamber of self contained cartridge with molecular grade
water with
Tris buffer, pH 9Ø
3. Fill appropriate chambers of self contained cartridge with extraction and
purification
reagents.
4. Load aliquot of clinical sample into appropriate chamber of self contained
cartridge.
5. Seal cartridge and load into GeneXpert instrument.
6. Run the appropriate extraction and amplification protocol on the GeneXpert
and
analyze the resultant data.
In yet another embodiment of the invention, the use of the primer probe with
the first
strand cDNA as described above to permit measurement of constituents of a Gene
Expression
Panel (Precision ProfileT) is performed using a QPCR assay on the Roche
LightCycler 480
Real-Time PCR System as follows:
Materials
1. 20X Primer/Probe stock for the 18S endogenous control gene. The endogenous
control gene may be dual labeled with either VIC-MGB or VIC-TAMRA.
2. 20X Primer/Probe stock for each target gene, dual labeled with either FAM-
TAMRA
or FAM-BHQ 1.
3. 2X LightCycler 490 Probes Master (master mix).
4. 1X cDNA sample stocks transcribed from RNA extracted from samples.
5. 1X TE buffer, pH 8Ø
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6. LightCycler 480 384-well plates.
7. Source MDx 24 gene Precision ProfileTm 96-well intermediate plates.
8. RNase/DNase free 96-well plate.
9. 1.5 mL microcentrifuge tubes.
10. Beckman/Coulter Biomek 3000 Laboratory Automation Workstation.
11. Velocityl l BravoTM Liquid Handling Platform.
12. LightCycler 480 Real-Time PCR System.
Methods
1. Remove a Source MDx 24 gene Precision ProfileTM 96-well intermediate plate
from
the freezer, thaw and spin in a plate centrifuge.
2. Dilute four (4) 1X cDNA sample stocks in separate 1.5 mL microcentrifuge
tubes
with the total final volume for each of 540 L.
3. Transfer the 4 diluted cDNA samples to an empty RNase/DNase free 96-well
plate
using the Biomek 3000 Laboratory Automation Workstation.
4. Transfer the cDNA samples from the cDNA plate created in step 3 to the
thawed and
centrifuged Source MDx 24 gene Precision ProfileTM 96-well intermediate plate
using
Biomek 3000 Laboratory Automation Workstation. Seal the plate with a foil
seal
and spin in a plate centrifuge.
5. Transfer the contents of the cDNA-loaded Source MDx 24 gene Precision
ProfileTM
96-well intermediate plate to a new LightCycler 480 384-well plate using the
BravoTM Liquid Handling Platform. Seal the 384-well plate with a LightCycler
480
optical sealing foil and spin in a plate centrifuge for 1 minute at 2000 rpm.
6. Place the sealed in a dark 4 C refrigerator for a minimum of 4 minutes.
7. Load the plate into the LightCycler 480 Real-Time PCR System and start the
LightCycler 480 software. Chose the appropriate run parameters and start the
run.
8. At the conclusion of the run, analyze the data and export the resulting CP
values to
the database.
In some instances, target gene FAM measurements may be beyond the detection
limit of
the particular platform instrument used to detect and quantify constituents of
a Gene Expression
Panel (Precision ProfileTo address the issue of "undetermined" gene expression
measures as
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lack of expression for a particular gene, the detection limit may be reset and
the "undetermined"
constituents may be "flagged". For example without limitation, the ABI Prism
7900HT
Sequence Detection System reports target gene FAM measurements that are beyond
the
detection limit of the instrument (>40 cycles) as "undetermined". Detection
Limit Reset is
performed when at least 1 of 3 target gene FAM CT replicates are not detected
after 40 cycles
and are designated as "undetermined". "Undetermined" target gene FAM CT
replicates are re-set
to 40 and flagged. CT normalization (0 CT) and relative expression
calculations that have used
re-set FAM CT values are also flagged.
Baseline profile data sets
The analyses of samples from single individuals and from large groups of
individuals
provide a library of profile data sets relating to a particular panel or
series of panels. These
profile data sets may be stored as records in a library for use as baseline
profile data sets. As the
term "baseline" suggests, the stored baseline profile data sets serve as
comparators for providing
a calibrated profile data set that is informative about a biological condition
or agent. Baseline
profile data sets may be stored in libraries and classified in a number of
cross-referential ways.
One form of classification may rely on the characteristics of the panels from
which the data sets
are derived. Another form of classification may be by particular biological
condition, e.g., lupus.
The concept of a biological condition encompasses any state in which a cell or
population of
cells may be found at any one time. This state may reflect geography of
samples, sex of subjects
or any other discriminator. Some of the discriminators may overlap. The
libraries may also be
accessed for records associated with a single subject or particular clinical
trial. The classification
of baseline profile data sets may further be annotated with medical
information about a particular
subject, a medical condition, and/or a particular agent.
The choice of a baseline profile data set for creating a calibrated profile
data set is related
to the biological condition to be evaluated, monitored, or predicted, as well
as, the intended use
of the calibrated panel, e.g., as to monitor drug development, quality control
or other uses. It
may be desirable to access baseline profile data sets from the same subject
for whom a first
profile data set is obtained or from different subject at varying times,
exposures to stimuli, drugs
or complex compounds; or may be derived from like or dissimilar populations or
sets of subjects.
The baseline profile data set may be normal, healthy baseline.
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The profile data set may arise from the same subject for which the first data
set is
obtained, where the sample is taken at a separate or similar time, a different
or similar site or in a
different or similar biological condition. For example, a sample may be taken
before stimulation
or after stimulation with an exogenous compound or substance, such as before
or after
therapeutic treatment. The profile data set obtained from the unstimulated
sample may serve as a
baseline profile data set for the sample taken after stimulation. The baseline
data set may also be
derived from a library containing profile data sets of a population or set of
subjects having some
defining characteristic or biological condition. The baseline profile data set
may also correspond
to some ex vivo or in vitro properties associated with an.in vitro cell
culture. The resultant
calibrated profile data sets may then be stored as a record in a database or
library along with or
separate from the baseline profile data base and optionally the first profile
data set al.though the
first profile data set would normally become incorporated into a baseline
profile data set under
suitable classification criteria. The remarkable consistency of Gene
Expression Profiles
associated with a given biological condition makes it valuable to store
profile data, which can be
used, among other things for normative reference purposes. The normative
reference can serve
to indicate the degree to which a subject conforms to a given biological
condition (healthy or
diseased) and, alternatively or in addition, to provide a target for clinical
intervention.
Calibrated data
Given the repeatability achieved in measurement of gene expression, described
above in
connection with "Gene Expression Panels" (Precision Profiles". ) and "gene
amplification", it
was concluded that where differences occur in measurement under such
conditions, the
differences are attributable to differences in biological condition. Thus, it
has been found that
calibrated profile data sets are highly reproducible in samples taken from the
same individual
under the same conditions. Similarly, it has been found that calibrated
profile data sets are
reproducible in samples that are repeatedly tested. Also found have been
repeated instances
wherein calibrated profile data sets obtained when samples from a subject are
exposed ex vivo to
a compound are comparable to calibrated profile data from a sample that has
been exposed to a
sample in vivo.
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Calculation of calibrated profile data sets and computational aids
The calibrated profile data set may be expressed in a spreadsheet or
represented
graphically for example, in a bar chart or tabular form but may also be
expressed in a three
dimensional representation. The function relating the baseline and profile
data may be a ratio
expressed as a logarithm. The constituent may be itemized on the x-axis and
the logarithmic
scale may be on the y-axis. Members of a calibrated data set may be expressed
as a positive
value representing a relative enhancement of gene expression or as a negative
value representing
a relative reduction in gene expression with respect to the baseline.
Each member of the calibrated profile data set should be reproducible within a
range with
1o respect to similar samples taken from the subject under similar conditions.
For example, the
calibrated profile data sets may be reproducible within 20%, and typically
within 10%. In
accordance with embodiments of the invention, a pattern of increasing,
decreasing and no change
in relative gene expression from each of a plurality of gene loci examined in
the Gene
Expression Panel (Precision Profile'm) may be used to prepare a calibrated
profile set that is
informative with regards to a biological condition, biological efficacy of an
agent treatment
conditions or for comparison to populations or sets of subjects or samples, or
for comparison to
populations of cells. Patterns of this nature may be used to identify likely
candidates for a drug
trial, used alone or in combination with other clinical indicators to be
diagnostic or prognostic
with respect to a biological condition or may be used to guide the development
of a
pharmaceutical or nutraceutical through manufacture, testing and marketing.
The numerical data obtained from quantitative gene expression and numerical
data from
calibrated gene expression relative to a baseline profile data set may be
stored in databases or
digital storage mediums and may be retrieved for purposes including managing
patient health
care or for conducting clinical trials or for characterizing a drug. The data
may be transferred in
physical or wireless networks via the World Wide Web, email, or internet
access site for
example or by hard copy so as to be collected and pooled from distant
geographic sites.
The method also includes producing a calibrated profile data set for the
panel, wherein
each member of the calibrated profile data set is a function of a
corresponding member of the
first profile data set and a corresponding member of a baseline profile data
set for the panel, and
wherein the baseline profile data set is related to the lupus or conditions
related to lupus to be
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evaluated, with the calibrated profile data set being a comparison between the
first profile data
set and the baseline profile data set, thereby providing evaluation of lupus
or conditions related
to lupus of the subject.
In yet other embodiments, the function is a mathematical function and is other
than a
simple difference, including a second function of the ratio of the
corresponding member of first
profile data set to the corresponding member of the baseline profile data set,
or a logarithmic
function. In such embodiments, the first sample is obtained and the first
profile data set
quantified at a first location, and the calibrated profile data set is
produced using a network to
access a database stored on a digital storage medium in a second location,
wherein the database
may be updated to reflect the first profile data set quantified from the
sample. Additionally,
using a network may include accessing a global computer network.
In an embodiment of the present invention, a descriptive record is stored in a
single
database or multiple databases where the stored data includes the raw gene
expression data (first
profile data set) prior to transformation by use of a baseline profile data
set, as well as a record of
the baseline profile data set used to generate the calibrated profile data set
including for example,
annotations regarding whether the baseline profile data set is derived from a
particular Signature
Panel and any other annotation that facilitates interpretation and use of the
data.
Because the data is in a universal format, data handling may readily be done
with a
computer. The data is organized so as to provide an output optionally
corresponding to a
graphical representation of a calibrated data set.
The above described data storage on a computer may provide the information in
a form
that can be accessed by a user. Accordingly, the user may load the information
onto a second
access site including downloading the information. However, access may be
restricted to users
having a password or other security device so as to protect the medical
records contained within.
A feature of this embodiment of the invention is the ability of a user to add
new or annotated
records to the data set so the records become part of the biological
information.
The graphical representation of calibrated profile data sets pertaining to a
product such as
a drug provides an opportunity for standardizing a product by means of the
calibrated profile,
more particularly a signature profile. The profile may be used as a feature
with which to
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demonstrate relative efficacy, differences in mechanisms of actions, etc.
compared to other drugs
approved for similar or different uses.
The various embodiments of the invention may be also implemented as a computer
program product for use with a computer system. The product may include
program code for
deriving a first profile data set and for producing calibrated profiles. Such
implementation may
include a series of computer instructions fixed either on a tangible medium,
such as a computer
readable medium (for example, a diskette, CD-ROM, ROM, or fixed disk), or
transmittable to a
computer system via a modem or other interface device, such as a
communications adapter
coupled to a network. The network coupling may be for example, over optical or
wired
communications lines or via wireless techniques (for example, microwave,
infrared or other
transmission techniques) or some combination of these. The series of computer
instructions
preferably embodies all or part of the functionality previously described
herein with respect to
the system. Those skilled in the art should appreciate that such computer
instructions can be
written in a number of programming languages for use with many computer
architectures or
operating systems. Furthermore, such instructions may be stored in any memory
device, such as
semiconductor, magnetic, optical or other memory devices, and may be
transmitted using any
communications technology, such as optical, infrared, microwave, or other
transmission
technologies. It is expected that such a computer program product may be
distributed as a
removable medium with accompanying printed or electronic documentation (for
example, shrink
wrapped software), preloaded with a computer system (for example, on system
ROM or fixed
disk), or distributed from a server or electronic bulletin board over a
network (for example, the
Internet or World Wide Web). In addition, a computer system is further
provided including
derivative modules for deriving a first data set and a calibration profile
data set.
The calibration profile data sets in graphical or tabular form, the associated
databases,
and the calculated index or derived algorithm, together with information
extracted from the
panels, the databases, the data sets or the indices or algorithms are
commodities that can be sold
together or separately for a variety of purposes as described in WO 01/25473.
In other embodiments, a clinical indicator may be used to assess the lupus or
conditions
related to lupus of the relevant set of subjects by interpreting the
calibrated profile data set in the
context of at least one other clinical indicator, wherein the at least one
other clinical indicator is
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selected from the group consisting of blood chemistry, molecular markers in
the blood (e.g.,
positive or negative titer from anti-nuclear antibody test or anti-RO (SSA),
other chemical
assays, and physical findings.
Index construction
In combination, (i) the remarkable consistency of Gene Expression Profiles
with respect
to a biological condition across a population or set of subject or samples, or
across a population
of cells and (ii) the use of procedures that provide substantially
reproducible measurement of
constituents in a Gene Expression Panel (Precision ProfileTM) giving rise to a
Gene Expression
Profile, under measurement conditions wherein specificity and efficiencies of
amplification for
all constituents of the panel are substantially similar, make possible the use
of an index that
characterizes a Gene Expression Profile, and which therefore provides a
measurement of a
biological condition.
An index may be constructed using an index function that maps values in a Gene
Expression Profile into a single value that is pertinent to the biological
condition at hand. The
values in a Gene Expression Profile are the amounts of each constituent of the
Gene Expression
Panel (Precision Profile"m) that corresponds to the Gene Expression Profile.
These constituent
amounts form a profile data set, and the index function generates a single
value-the index-
from the members of the profile data set.
The index function may conveniently be constructed as a linear sum of terms,
each term
being what is referred to herein as a "contribution function" of a member of
the profile data set.
For example, the contribution function may be a constant times a power of a
member of the
profile data set. So the index function would have the form
I =ECiMiP(O ,
where I is the index, Mi is the value of the member i of the profile data set,
Ci is a
constant, and P(i) is a power to which Mi is raised, the sum being formed for
all integral values
of i up to the number of members in the data set. We thus have a linear
polynomial expression.
The role of the coefficient Ci for a particular gene expression specifies
whether a higher ACt
value for this gene either increases (a positive Ci) or decreases (a lower
value) the likelihood of
lupus, the ACt values of all other genes in the expression being held
constant.
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The values Ci and P(i) may be determined in a number of ways, so that the
index I is
informative of the pertinent biological condition. One way is to apply
statistical techniques, such
as latent class modeling, to the profile data sets to correlate clinical data
or experimentally
derived data, or other data pertinent to the biological condition. In this
connection, for example,
may be employed the software from Statistical Innovations, Belmont,
Massachusetts, called
Latent Gold . Alternatively, other simpler modeling techniques may be employed
in a manner
known in the art. The index function for lupus may be constructed, for
example, in a manner that
a greater degree of lupus (as determined by the profile data set for the
Precision Profile'a' for
Lupus shown in Table 1 or Precision Profile'T' for Inflammatory Response shown
in Table 2)
lo correlates with a large value of the index function. As discussed in
further detail below, a
meaningful lupus index that is proportional to the expression, was constructed
as follows:
5.5 + .71 {SGK} - {LGALS3BP}
where the braces around a constituent designate measurement of such
constituent and the
constituents are a subset of the Precision ProfileTm for Lupus shown in Table
1 or Precision
Profile"m for Inflammatory Response shown in Table 2.
Just as a baseline profile data set, discussed above, can be used to provide
an appropriate
normative reference, and can even be used to create a Calibrated profile data
set, as discussed
above, based on the normative reference, an index that characterizes a Gene
Expression Profile
can also be provided with a normative value of the index function used to
create the index. This
normative value can be determined with respect to a relevant population or set
of subjects or
samples or to a relevant population of cells, so that the index may be
interpreted in relation to the
normative value. The relevant population or set of subjects or samples, or
relevant population of
cells may have in conunon a property that is at least one of age range,
gender, ethnicity,
geographic location, nutritional history, medical condition, clinical
indicator, medication,
physical activity, body mass, and environmental exposure.
As an example, the index can be constructed, in relation to a normative Gene
Expression
Profile for a population or set of healthy subjects, in such a way that a
reading of approximately
1 characterizes normative Gene Expression Profiles of healthy subjects. Let us
further assume
that the biological condition that is the subject of the index is lupus; a
reading of 1 in this
example thus corresponds to a Gene Expression Profile that matches the norm
for healthy
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subjects. A substantially higher reading then may identify a subject
experiencing lupus, or a
condition related to lupus. The use of 1 as identifying a normative value,
however, is only one
possible choice; another logical choice is to use 0 as identifying the
normative value. With this
choice, deviations in the index from zero can be indicated in standard
deviation units (so that
values lying between -1 and +1 encompass 90% of a normally distributed
reference population or
set of subjects. Since it was determined that Gene Expression Profile values
(and accordingly
constructed indices based on them) tend to be normally distributed, the 0-
centered index
constructed in this manner is highly informative. It therefore facilitates use
of the index in
diagnosis of disease and setting objectives for treatment.
Still another embodiment is a method of providing an index pertinent to lupus
or
conditions related to lupus of a subject based on a first sample from the
subject, the first sample
providing a source of RNAs, the method comprising deriving from the first
sample a profile data
set, the profile data set including a plurality of members, each member being
a quantitative
measure of the amount of a distinct RNA constituent in a panel of constituents
selected so that
measurement of the constituents is indicative of the presumptive signs of
lupus, the panel
including at least two of the constituents of any of the genes listed in the
Precision Profile for
LupusTm (Table 1) or the Precision Profile'm for Inflammatory Response (Table
2). In deriving
the profile data set, such measure for each constituent is achieved under
measurement conditions
that are substantially repeatable, at least one measure from the profile data
set is applied to an
index function that provides a mapping from at least one measure of the
profile data set into one
measure of the presumptive signs of lupus, so as to produce an index pertinent
to the lupus or
conditions related to lupus of the subject.
As another embodiment of the invention, an index function I of the form
I=Co +E Ci11'IliPI (i) M2; P2(i)
,
can be employed, where MI and M2 are values of the member i of the profile
data set, C;
is a constant determined without reference to the profile data set, and P 1
and P2 are powers to
which M, and MZ are raised. The role of P1(i) and P2(i) is to specificy the
specific functional
form of the quadratic expression, whether in fact the equation is linear,
quadratic, contains cross-
product terms, or is constant. For example, when P1 = P2 = 0, the index
function is simply the
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sum of constants; when P1 = 1 and P2 = 0, the index function is a linear
expression; when P1 =
P2 =1, the index function is a quadratic expression.
The constant Co serves to calibrate this expression to the biological
population of interest
that is characterized by having lupus. In this embodiment, when the index
value equals 0, the
odds are 50:50 of the subject having lupus vs a normal subject. More
generally, the predicted
odds of the subject having lupus is [exp(I;)], and therefore the predicted
probability of having
lupus is [exp(I;)]/[l+exp((I;)]. Thus, when the index exceeds 0, the predicted
probability that a
subject has lupus is higher than .5, and when it falls below 0, the predicted
probability is less
than .5.
The value of Co may be adjusted to reflect the prior probability of being in
this population
based on known exogenous risk factors for the subject. In an embodiment where
Co is adjusted
as a function of the subject's risk factors, where the subject has prior
probability p; of having
lupus based on such risk factors, the adjustment is made by increasing
(decreasing) the
unadjusted Co value by adding to Co the natural logarithm of the following
ratio: the prior odds
of having lupus taking into account the risk factors/ to the overall prior
odds of having lupus
without taking into account the risk factors.
Performance and Accuracy Measures of the Invention
The performance and thus absolute and relative clinical usefulness of the
invention may
be assessed in multiple ways as noted above. Amongst the various assessments
of performance,
the invention is intended to provide accuracy in clinical diagnosis and
prognosis. The accuracy
of a diagnostic or prognostic test, assay, or method concerns the ability of
the test, assay, or
method to distinguish between subjects having lupus is based on whether the
subjects have an
"effective amount" or a "significant alteration" in the levels of a lupus
associated gene. By
"effective amount" or "significant alteration", it is meant that the
measurement of an appropriate
number of lupus associated gene (which may be one or more) is different than
the predetermined
cut-off point (or threshold value) for that lupus associated gene and
therefore indicates that the
subject has lupus for which the lupus associated gene(s) is a determinant.
The difference in the level of lupus associated gene(s) between normal and
abnormal is
preferably statistically significant. As noted below, and without any
limitation of the invention,
achieving statistical significance, and thus the preferred analytical and
clinical accuracy,
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generally but not always requires that combinations of several lupus
associated gene(s) be used
together in panels and combined with mathematical algorithms in order to
achieve a statistically
significant lupus associated gene index.
In the categorical diagnosis of a disease state, changing the cut point or
threshold value of
a test (or assay) usually changes the sensitivity and specificity, but in a
qualitatively inverse
relationship. Therefore, in assessing the accuracy and usefulness of a
proposed medical test,
assay, or method for assessing a subject's condition, one should always take
both sensitivity and
specificity into account and be mindful of what the cut point is at which the
sensitivity and
specificity are being reported because sensitivity and specificity may vary
significantly over the
range of cut points. Use of statistics such as AUC, encompassing all potential
cut point values, is
preferred for most categorical risk measures using the invention, while for
continuous risk
measures, statistics of goodness-of-fit and calibration to observed results or
other gold standards,
are preferred.
Using such statistics, an "acceptable degree of diagnostic accuracy", is
herein defined as
a test or assay (such as the test of the invention for determining an
effective amount or a
significant alteration of lupus associated gene(s), which thereby indicates
the presence of a lupus
in which the AUC (area under the ROC curve for the test or assay) is at least
0.60, desirably at
least 0.65, more desirably at least 0.70, preferably at least 0.75, more
preferably at least 0.80, and
most preferably at least 0.85.
By a "very high degree of diagnostic accuracy", it is meant a test or assay in
which the
AUC (area under the ROC curve for the test or assay) is at least 0.75,
desirably at least 0.775,
more desirably at least 0.800, preferably at least 0.825, more preferably at
least 0.850, and most
preferably at least 0.875.
The predictive value of any test depends on the sensitivity and specificity of
the test, and
on the prevalence of the condition in the population being tested. This
notion, based on Bayes'
theorem, provides that the greater the likelihood that the condition being
screened for is present
in an individual or in the population (pre-test probability), the greater the
validity of a positive
test and the greater the likelihood that the result is a true positive. Thus,
the problem with using
a test in any population where there is a low likelihood of the condition
being present is that a
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positive result has limited value (i.e., more likely to be a false positive).
Similarly, in
populations at very high risk, a negative test result is more likely to be a
false negative.
As a result, ROC and AUC can be misleading as to the clinical utility of a
test in low
disease prevalence tested populations (defined as those with less than 1% rate
of occurrences
(incidence) per annum, or less than 10% cumulative prevalence over a specified
time horizon).
Alternatively, absolute risk and relative risk ratios as defined elsewhere in
this disclosure can be
employed to determine the degree of clinical utility. Populations of subjects
to be tested can also
be categorized into quartiles by the test's measurement values, where the top
quartile (25% of the
population) comprises the group of subjects with the highest relative risk for
developing lupus,
and the bottom quartile comprising the group of subjects having the lowest
relative risk for
developing lupus. Generally, values derived from tests or assays having over
2.5 times the
relative risk from top to bottom quartile in a low prevalence population are
considered to have a
"high degree of diagnostic accuracy," and those with five to seven times the
relative risk for each
quartile are considered to have a "very high degree of diagnostic accuracy."
Nonetheless, values
derived from tests or assays having only 1.2 to 2.5 times the relative risk
for each quartile remain
clinically useful are widely used as risk factors for a disease. Often such
lower diagnostic
accuracy tests must be combined with additional parameters in order to derive
meaningful
clinical thresholds for therapeutic intervention, as is done with the
aforementioned global risk
assessment indices.
A health economic utility function is yet another means of measuring the
performance
and clinical value of a given test, consisting of weighting the potential
categorical test outcomes
based on actual measures of clinical and economic value for each. Health
economic
performance is closely related to accuracy, as a health economic utility
function specifically
assigns an economic value for the benefits of correct classification and the
costs of
misclassification of tested subjects. As a performance measure, it is not
unusual to require a test
to achieve a level of performance which results in an increase in health
economic value per test
(prior to testing costs) in excess of the target price of the test.
In general, alternative methods of determining diagnostic accuracy are
commonly used
for continuous measures, when a disease category or risk category (such as
those at risk for
having a bone fracture) has not yet been clearly defined by the relevant
medical societies and
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practice of medicine, where thresholds for therapeutic use are not yet
established, or where there
is no existing gold standard for diagnosis of the pre-disease. For continuous
measures of risk,
measures of diagnostic accuracy for a calculated index are typically based on
curve fit and
calibration between the predicted continuous value and the actual observed
values (or a historical
index calculated value) and utilize measures such as R squared, Hosmer-
Lemeshow P-value
statistics and confidence intervals. It is not unusual for predicted values
using such algorithms to
be reported including a confidence interval (usually 90% or 95% CI) based on a
historical
observed cohort's predictions, as in the test for risk of future breast cancer
recurrence
commercialized by Genomic Health, Inc. (Redwood City, California).
In general, by defining the degree of diagnostic accuracy, i.e., cut points on
a ROC curve,
defining an acceptable AUC value, and determining the acceptable ranges in
relative
concentration of what constitutes an effective amount of the lupus associated
gene(s) of the
invention allows for one of skill in the art to use the lupus associated
gene(s) to identify,
diagnose, or prognose subjects with a pre-determined level of predictability
and performance.
Results from the lupus associated gene(s) indices thus derived can then be
validated
through their calibration with actual results, that is, by comparing the
predicted versus observed
rate of disease in a given population, and the best predictive lupus
associated gene(s) selected for
and optimized through mathematical models of increased complexity. Many such
formula may
be used; beyond the simple non-linear transformations, such as logistic
regression, of particular
interest in this use of the present invention are structural and synactic
classification algorithms,
and methods of risk index construction, utilizing pattern recognition
features, including
established techniques such as the Kth-Nearest Neighbor, Boosting, Decision
Trees, Neural
Networks, Bayesian Networks, Support Vector Machines, and Hidden Markov
Models, as well
as other formula described herein.
Furthermore, the application of such techniques to panels of multiple lupus
associated
gene(s) is provided, as is the use of such combination to create single
numerical "risk indices" or
"risk scores" encompassing information from multiple lupus associated gene(s)
inputs.
Individual B lupus associated gene(s) may also be included or excluded in the
panel of lupus
associated gene(s) used in the calculation of the lupus associated gene(s)
indices so derived
above, based on various measures of relative performance and calibration in
validation, and
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employing through repetitive training methods such as forward, reverse, and
stepwise selection,
as well as with genetic algorithm approaches, with or without the use of
constraints on the
complexity of the resulting lupus associated gene(s) indices.
The above measurements of diagnostic accuracy for lupus associated gene(s) are
only a
few of the possible measurements of the clinical performance of the invention.
It should be
noted that the appropriateness of one measurement of clinical accuracy or
another will vary
based upon the clinical application, the population tested, and the clinical
consequences of any
potential misclassification of subjects. Other important aspects of the
clinical and overall
performance of the invention include the selection of lupus associated gene(s)
so as to reduce
overall lupus associated gene(s) variability (whether due to method
(analytical) or biological
(pre-analytical variability, for example, as in diurnal variation), or to the
integration and analysis
of results (post-analytical variability) into indices and cut-off ranges), to
assess analyte stability
or sample integrity, or to allow the use of differing sample matrices amongst
blood, cells, serum,
plasma, urine, etc.
Kits
The invention also includes a lupus detection reagent, i.e., nucleic acids
that specifically
identify one or more lupus or condition related to lupus nucleic acids (e.g.,
any gene listed in
Tables 1-7, 9-13, and 15-20; sometimes referred to herein as lupus associated
genes or lupus
associated constituents) by having homologous nucleic acid sequences, such as
oligonucleotide
sequences, complementary to a portion of the lupus genes nucleic acids or
antibodies to proteins
encoded by the lupus genes nucleic acids packaged together in the form of a
kit. The
oligonucleotides can be fragments of the lupus genes. For example the
oligonucleotides can be
200, 150, 100, 50, 25, 10 or less nucleotides in length. The kit may contain
in separate
containers a nucleic acid or antibody (either already bound to a solid matrix
or packaged
separately with reagents for binding them to the matrix), control fonnulations
(positive and/or
negative), and/or a detectable label. Instructions (i.e., written, tape, VCR,
CD-ROM, etc.) for
carrying out the assay may be included in the kit. The assay may for example
be in the form of
PCR, a Northern hybridization or a sandwich ELISA, as known in the art.
For example, lupus gene detection reagents can be immobilized on a solid
matrix such as
a porous strip to form at least one lupus gene detection site. The measurement
or detection
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region of the porous strip may include a plurality of sites containing a
nucleic acid. A test strip
may also contain sites for negative and/or positive controls. Alternatively,
control sites can be
located on a separate strip from the test strip. Optionally, the different
detection sites may
contain different amounts of immobilized nucleic acids, i.e., a higher amount
in the first
detection site and lesser amounts in subsequent sites. Upon the addition of
test sample, the
number of sites displaying a detectable signal provides a quantitative
indication of the amount of
lupus genes present in the sample. The detection sites may be configured in
any suitably
detectable shape and are typically in the shape of a bar or dot spanning the
width of a test strip.
Alternatively, lupus detection genes can be labeled (e.g., with one or more
fluorescent
dyes) and immobilized on lyophilized beads to form at least one lupus gene
detection site. The
beads may also contain sites for negative and/or positive controls. Upon
addition of the test
sample, the number of sites displaying a detectable signal provides a
quantitative indication of
the amount of lupus genes present in the sample.
Alternatively, the kit contains a nucleic acid substrate array comprising one
or more
nucleic acid sequences. The nucleic acids on the array specifically identify
one or more nucleic
acid sequences represented by lupus genes (see Tables 1-7, 9-13, an d 15-20).
In various
embodiments, the expression of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 40
or 50 or more of the
sequences represented by lupus genes (see Tables 1-7, 9-13, and 15-20) can be
identified by
virtue of binding to the array. The substrate array can be on, i.e., a solid
substrate, i.e., a"chip"
as described in U.S. Patent No. 5,744,305. Alternatively, the substrate array
can be a solution
array, i.e., Luminex, Cyvera, Vitra and Quantum Dots' Mosaic.
The skilled artisan can routinely make antibodies, nucleic acid probes, i.e.,
oligonucleotides, aptamers, siRNAs, antisense oligonucleotides, against any of
the lupus genes
listed in Tables 1-7, 9-13, and 15-20.
Other Embodiments
While the invention has been described in conjunction with the detailed
description
thereof, the foregoing description is intended to illustrate and not limit the
scope of the invention,
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which is defined by the scope of the appended claims. Other aspects,
advantages, and
modifications are within the scope of the following claims.
EXAMPLES
Example 1: Pilot Study: Lupus Clinical Data (DLE, SCLE, and LET) Analyzed with
Latent
Class Modeling Based on The Precision Profile"m for Lupus:
RNA was isolated using the PAXgeneTm System from blood samples obtained from a
total
of 16 subjects with a confirmed diagnosis of discoid lupus erythematosus
(DLE), 11 subjects
diagnosed with subacute cutaneous lupus erythematosus (SCLE), 13 subjects
diagnosed with
lupus tumidus erythematosus (LET), 10 healthy study volunteers (HV) and 50
Source MDx
normal subjects (Normal).
From a targeted 134 -gene Precision ProfileTm for Lupus (shown in Table 1),
selected to
be informative relative to biological state of lupus patients, primers and
probes were prepared for
48 genes. Each of these genes was evaluated for significance (i.e., p-value)
regarding their
ability to discriminate between subjects afflicted with lupus (DLE, SLE, and
LET) and subjects
without lupus (i.e., Normal and HV subjects). A ranking of the top 48 genes is
shown in Tables
3-5, summarizing the results of 2 different significance tests for the
difference in the mean
expression levels for Normal and HV subjects and subjects suffering from lupus
(DLE, SCLE
and LET). Since competing methods are available that are justified under
different assumptions,
the p-values were computed in 2 different ways:
1) Based on GOLDMineR's ordignal logit model. This approach assumes that the
gene
expression is ordered on an interval scale with optimal scores estimated for
each group
individually (with extreme optimal scores ranging from 0 and 1, assigned to
DLE, SCLE,
LET, HV, and Normal subjects, respectively). The genes are ranked from most to
least
significant according to their p-value (Table 3).
2) Based on stepwise logistic regression (STEP analysis), where group
membership (i.e.,
Normal (excluding HV) vs. lupus (SCLE, DLE and LET) (Table 4), or non-lupus
(Normal +
HV) vs. lupus (SCLE, DLE and LET) (Table 5)) is predicted as a function of the
gene
expression.
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As expected, a comparison of the two different approaches yielded comparable p-
values
and comparable rankings for the genes (shown in Tables 3, 4, and 5). The most
significant genes
are shaded in gray in Table 3. Based on the optimal scores estimated for each
group using the
ordinal logit model, DLE and SCLE subjects were similar (at the low end of the
0 to 1 score
scale), while HV and Normal subjects were similar (at the high end of the 0 to
1 score scale),
with LET being somewhere in the middle of the score scale. Thus, the
significant group mean
differences were largely between SCLE and DLE lupus and non-lupus (Normals and
HV). This
suggests that somewhat more significant results might be obtained if the LET
group were
excluded from gene expression model development (see Example 2).
LGALS3BP and was found to be the most significant gene at the 0.05 level using
STEP
analysis (as shown in Tables 4 and 5) and was subject to further stepwise
logistic regression
using two different types of analyses to generate 2-gene models capable of
correctly classifying
lupus versus and non-lupus subjects with at least 75% accuracy, as described
below.
Gene Expression ModelinQ
Gene expression profiles were obtained using the 48 genes from the Precision
Profile'm for
Lupus shown in Table 1, and the Search procedure in GOLDMineR (Magidson, 1998)
to
implement stepwise logistic regressions (STEP analysis) for predicting the
dichotomous variable
that distinguishes 1) subjects suffering from lupus (including SCLE, DLE and
LET) from
Normal subjects (excluding HV subjects) as a function of the 48 genes (ranked
in Table 4); and
2) subjects suffering from lupus (SCLE, DLE, and LET) from non-lupus subjects
(Normal + HV
subjects), as a function of the 48 genes (ranked in Table 5). The STEP
analysis was performed
under the assumption that the gene expressions follow a multinormal
distribution, with different
means and different variance-covariance matrices for the Normal, HV and lupus
populations.
LGALS3BP was subject to a further analysis in a 2-gene model where a1147
remaining
genes were evaluated as the second gene in this 2-gene model. All models that
yielded
significant incremental p-values, at the 0.05 level, for the second gene were
then analyzed using
Latent Gold to to determine classification percentages.
RZ was also reported. The R2 statistic is a less formal statistical measure of
goodness of
prediction, which varies between 0 (predicted probability of having lupus is
constant regardless
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of OCt values on the 2 genes) to 1(predicted probability of having lupus = 1
for each lupus
subject, and = 0 for each Normal/HV subject).
Both types of analyses yielded the same 2-gene-model, LGALS3BP and SGK, as
shown
in Tables 6 and 7, and plotted in Figure 1(note: although not all 5 groups
were included in both
analyses, all 5 are identified in the graph). As shown in Table 8, the 2-gene
model LGALS3BP
and SGK correctly classified Normal subjects with 97% accuracy, DLE subjects
with 81%
accuracy, SCLE subjects with 91% accuracy, and LET subjects with only 54%.
As can be seen from Figure 1, these 2 genes do not discriminate between LET
and
Normals very well. However, the model does do well in discriminating SCLE and
DLE types of
lupus from Normals. Not counting the LET subjects, only 4 lupus and 2 Normals
are
misclassified. In addition, as shown in Figure 1, the HV population is very
similar to the
Normals in that both are primarily above the discrimination line shown, none
are misclassified.
The discrimination line shown in Figure 1 is an example of the Index Function
evaluated
at a particular logit (log odds) value. Values above and to the left of the
line are predicted to be
in the non-lupus population (Normal and HV), those below and to the right of
the line in the
lupus population (SCLE and DLE). This is a simplified version of the "Index
function" as
displayed in two dimensions, where the gene with positive coefficients
(positive contributions)
(SGK) is plotted along the horizontal axis, and the gene with negative
coefficients (LGALS3BP)
is plotted along the vertical axis. `Positive' coefficients means that the
higher the ACt values for
those genes (holding the other genes constant) increases the predicted logit,
and thus the
predicted probability of being in the diseased group.
The intercept (alpha) and slope (beta) of the discrimination line was computed
according
to the data as follows:
A cutoff of 0.4350102 was used to compute alpha (equals
-0.261438 in logit units).
The following equation is given below the graph shown in Figure 1:
Lupus Discrimination Line: LGALS3BP = 5.5 + 0.71 * SGK.
Subjects below and to the right of this discrimination line have a predicted
probability of
being in the diseased group higher than the cutoff probability of 0.4350102.
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The intercept Co = 5.5 was computed by taking the difference between the
intercepts for
the 2 groups [6.7622 -(-6.7622)= 13.5244]. This quantity was then multiplied
by -1/X where X
is the coefficient for LGALS3BP (-2.3474), then the log-odds of the cutoff
probability (-
0.261438) was subtracted.
For comparison, a custom 2-gene model was developed using the ordinal
algorithm of
GOLDMineR, based on a115 groups starting with the 2 d best gene identified in
the earlier
stepwise analysis, IFI6 (as shown in Tables 4 and 5). Optimal scores for each
group were
obtained from GOLDMineR (DLE = 0, SCLE = 0.47, LET = 0.499, HV = 0.803,
Normals =
1.0). All cases were sorted based on their predicted odds of being DLE versus
Normal, since the
extreme optimal scores of 0 and 1 were assigned to these two groups
respectively.
The resulting 2 genes, IFI6 and THBSl, are shown in Table 9 and in Figure 2.
Figure 2
shows that results are similar to that of Figure 1 except that only 2 lupus
subjects are
misclassified, along with 1 Normal and HV, when the LET subjects are not
counted.
The following equation is given below the graph shown in Figure 2:
Lupus Discrimination Line: THBS1 = 40.7-1.5 1 *IFI6.
Subjects below and to the left of this discrimination line have a predicted
DLE v. Normal odds of
less than 2. The lower the odds the less likely to be normal; the higher the
odds, the more likely
to be normal. The intercept Co = 40.7 is the number that provides the
predicted odds of 2Ø
Classification rates for this 5-group model were computed based on a DLE v.
Normal
odds cutoff of 2.0, the results are as follows: 14 of the 16 DLE subjects and
all 11 SCLE subjects
were correctly classified by the 2-gene model, IFI6 and THBS 1, in the "lupus
group; 49 or the 50 Normal subjects and 9 of the 10 HV subjects were correctly
classified by
this model as "normal".
Example 2: Lupus Clinical Data (DLE and SCLE, LET excluded) Analyzed with
Latent
Class Modeling Based on The Precision ProfileTM for Lupus:
The data analysis shown in Example 1 above was reanalyzed by stepwise
regression,
excluding the LET data points from the model development to generate multi-
gene models
capable of distinguishing between lupus (DLE and SCLE) and non-lupus (Normal
and HV)
subjects. Two different types of analyses were performed. The first analysis
was based on an
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ordinal logit for the 4 groups (excluding LET). However 2 groups (DLE and
SCLE) were scored
1 and HV and Normals were scored 0, so the analaysis was equivalent to a 2-
group analysis. In
the second analysis, all four groups were considered distinct, with the
ordinal logit algorithm
from GOLDMineR used to assign scores for each of the four groups individually.
For both of
these analyses, OASL was selected as the first gene. The resulting ranking of
genes based on
these two types of analyses are shown in Tables 10 and 11, respectively.
OASL was subject to a further analysis in a 2-gene model where all 47
remaining genes
were evaluated as the second gene in a 2-gene model. All models that yielded
significant
incremental p-values, at the 0.05 level, for the second gene were then
analyzed using Latent Gold
1o to determine classification percentages. R2 was also reported as described
above in Example 1.
The combined DLE and SCLE, and Normal and HV stepwise regression analysis.
(excluding LET) yielded the 2-gene model, OASL and IL6 (shown in Table 12 and
Figure 3).
Classification rates were computed for this 2-gene model based on a DLE v.
Normal odds cutoff
of 2Ø The classification rates are as follows: 15 of the 16 DLE subjects and
10 of the 1.1 SCLE
subjects were correctly classified into the "lupus" group; all 10 HV subjects
and 49 of the 50
Normal subjects were correctly classified into the "normal" group.
The stepwise regression analysis where each of the four groups remained
distinct yielded
the 2-gene model OASL and THBS 1(shown in Table 13). As can be seen from Table
14, the 2-
gene model OASL and THBSl correctly classified Normal subjects with 98%
accuracy, DLE
subjects with 88% accuracy, and SCLE subjects with 91% accuracy. These results
are depicted
graphically in Figure 4.
The resulting 2-gene models from both types of analyses are plotted in Figures
3 and 4,
respectively (note that LET data points were not included in the analyses,
however LET data
points are identified in Figures 3 and 4). The following equation is given
below the graph shown
in Figure 3:
Lupus Discrimination Line: OASL = 29.4 -0.521 * IL6.
Subjects below and to the left of this discrimination line have a predicted
probability of
being in the diseased group higher than the cutoff probability of 0.5.
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The intercept Co = 29.4 was computed by taking the difference between the
intercepts for
the 2 groups SCLE and Normals [35.3-(-35.3) = 70.6]. This quantity was then
multiplied by -
1/X where X is the coefficient for OASL (-2.4).
The following equation is given below the graph shown in Figure 4:
Lupus Discrimination Line: OASL = 22.1 -0.33* THBS1.
Subjects below and to the left of this discrimination line have a predicted
probability of
being in the diseased group higher than the cutoff probability of 0.565.
The intercept Co = 22.1 was computed by taking the difference between the
intercepts for
the 2 groups SCLE and Normals [22.64-(-22.64) = 45.3]. This quantity was then
multiplied by -
1/X where X is the coefficient for OASL (-2.02).
The results shown in Figures 3 and 4 are better than the results plotted in
Figure 1, which
included LET subjects in the analysis. The OASL and IL6 model shown in Figure
3 has only 2
lupus and 1 Normal (and zero HV) subjects misclassified. The OASL and THBS1
model shown
in Figure 4 has only 1 lupus and 1 Normal (and zero HV) subjects
misclassified.
As a comparison, a stepwise regression analysis to identify a gene model
capable of
distinguishing of the LET subjects from HV subjects and Normal subjects. was
performed. For
this analysis, LGALS3BP was selected as the first gene, ranked as shown in
Table 15. This
analysis yielded the 2-gene model LGAS3BP and CCR10. As can be seen from the
results after
2-steps of stepwise regression, shown in Table 16, this model did not perform
as well as the
others that discriminate the other types of lupus, DLE and SCLE from Normal/HV
(note the
lower R2 value 0.512 in the second stepwise regression, versus R2 value 0.813
in the second
stepwise regression for the 2-gene model OASL and IL6). The results indicate
the genes that
were measured are more sensitive to DLE and SCLE differences from Normal, than
to LET
differences.
Example 3: Additional Lupus Models Based on the Precision Profile'm for Lupus:
Additional stepwise regression analysis was performed on the clinical data
described in
Tables 10 and 15 from Example 2 to identify a comprehensive list of additional
2 and 3 gene
models that discriminate between the following groups 1) Combined DLE/SCLE vs.
Combined
HV/Normal subjects (Table 10), and 2) LET vs. HV and Normal subjects (Table
15). For al12-
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gene models, both genes needed to have significant incremental p-values (p <
0.05) to be
retained in the 2-gene model. For 3-gene models, a113 genes needed to have
significant
incremental p-values (p < 0.05) to be retained in the 3-gene model. Al12-gene
and 3-gene
models also needed to reach the 75%/75% correct classification rate threshold
to be retained.
For each of analysis, the following 7 low expressing genes were excluded:
CCL17, CCL19,
CCL24, IL12B, II.4, IL6, and SELE. The gene CCL2 was a borderline low-
expressing gene, but
was included in each analysis.
Only the most signficant genes from Tables 10 and 15 were considered as being
the 1 st
gene in a potential 2-gene model. The cutoff p-values were p = 3.7E-08 for the
Combined
lo DLE/SCLE vs. Combined HV/Normal models (p-values shown in Table 10) and p =
8.9E-05 for
the LET vs. HV and Normal models (p-values shown in Table 15). Each of the
genes with
significant p-values meeting the designated cutoff were subject to stepwise
regression analysis
where a1147 remaining genes from Table 10 or 15 were were evaluated as the
second gene in a
2-gene model. Each 2-gene model identified having significant incremental p-
values (p < 0.05)
for both genes and that reached the 75%/75% correct classification rate was
subject to another
round of stepwise regression analysis where all 46 remaining genes from Table
10 or 15 were
evaluated as the third gene in a 3 gene model.
A list of a112-gene and 3-gene models that met the designated criteria and
discriminate
between DLE/SCLE subjects and HV/Normal subjects with at least 75%/75%
accuracy are
shown in Tables 17 and 18, respectively.
A listing of al12-gene and 3-gene models that met the designated criteria and
discriminate between LET subjects and HV/Normal subjects with at least 75%/75%
accuracy are
shown in Tables 19 and 20, respesctively. Two of the 2-gene models shown in
Table 19 (IL6ST
and THBS 1; CALR and CCL2) did not meet the 75%/75% criteria. However, after
another round
of stepwise regression, when a 3d gene was added to these models, the 3-gene
model met the
75%/75% threshold, as shown in Table 20.
These data support that Gene Expression Profiles with sufficient precision and
calibration
as described herein (1) can determine subsets of individuals with a known
biological condition,
particularly individuals with lupus or individuals with conditions related to
lupus; (2) may be
used to monitor the response of patients to therapy; (3) may be used to assess
the efficacy and
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safety of therapy; and (4) may be used to guide the medical management of a
patient by
adjusting therapy to bring one or more relevant Gene Expression Profiles
closer to a target set of
values, which may be normative values or other desired or achievable values.
Gene Expression Profiles are used for characterization and monitoring of
treatment
efficacy of individuals with lupus, or individuals with conditions related to
lupus. Use of the
algorithmic and statistical approaches discussed above to achieve such
identification and to
discriminate in such fashion is within the scope of various embodiments
herein.
The references listed below are hereby incorporated herein by reference.
References
Magidson, J. GOLDMineR User's Guide (1998). Belmont, MA: Statistical
Innovations Inc.
Vermunt J.K. and J. Magidson. Latent GOLD 4.0 User's Guide. (2005) Belmont,
MA: Statistical
Innovations Inc.
Vermunt J.K. and J. Magidson. Technical Guide for Latent GOLD 4.0: Basic and
Advanced
(2005)
Belmont, MA: Statistical Innovations Inc.
Vermunt J.K. and J. Magidson. Latent Class Cluster Analysis in (2002) J. A.
Hagenaars and A.
L. McCutcheon (eds.), Applied Latent Class Analysis, 89-106. Cambridge:
Cambridge
University Press.
Magidson, J. "Maximum Likelihood Assessment of Clinical Trials Based on an
Ordered
Categorical Response." (1996) Drug Information Journal, Maple Glen, PA: Drug
Information
Association, Vol. 30, No. 1, pp 143-170.
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TABLE 1: Precision ProfileTM for Lupus:
Sou, r.ce MDx Lu us ene Panel
~ Gene Gene Name Gene ccession
bol Number
_
ADAM17 a disintegrin and metalloproteinase domain 17 (tumor necrosis NM_003183
factor, alpha, converting en e
ADAM9 a disintegrin and metalloproteinase domain 9 (meltrin gamma) NM
001005845
AGRIN agrin NM 198576
APOBECI a oli o rotein B mRNA editing enzyme, catalytic polypeptide 1 NM
001644
BAX BCL2-associated X protein NM 138761
BIRC4BP XIAP associated factor-1 NM 017523
BSTI bone marrow stromal cell antigen I NM 004334
CIQA complement component 1, subcomponent, A chain NM-015991
CALR calreticulin NM 004343
CASP3 cas ase 3, a o tosis-related cysteine peptidase NM 004346
CCL17 chemokine (C-C motif) ligand 17 NM 002987
CCL19 chemokine (C-C moti li and.19 NM 006274
CCL2 chemokine (C-C moti ligand 2 NM 002982
CCL24 chemokine (C-C moti ligand 24 NM 002991
CCL27 chemokine (C-C moti ligand 27 NM 006664
CCL3 chemokine (C-C moti ligand 3 NM 002983
CCR10 chemokine (C-C moti receptor 10 NM 016602
CD19 CD19 Antigen NM 001770
CD3Z CD3 Anti en, Zeta Pol e tide NM 198053
CD4 CD4 antigen (p55) NM 000616
CD40 CD40 antigen (TNF receptor superfamily member 5) NM 152854
CD68 CD68 antigen NM 001251
CD69 CD69 antigen (p60, early T-cell activation anti en NM 001781
CD8A CD8 antigen, alpha polypeptide NM 001768
CIC capicua homolog Droso hila NM 015125
CR1 complement component (3b/4b) receptor 1, including Knops NM_000573
blood group system
CREB5 cAMP responsive element binding protein 5 NM 182898
CRP C-reactive protein, pentraxin-related NM 000567
CSF2 colony stimulating factor 2 ranuloc e-macro ha e NM 000758
CSF3 Colony stimulating factor 3 ranuloc es NM 000759
CTLA4 cytotoxic T-I m hoc e-associated protein 4 NM 005214
CXCL1 chemokine (C-X-C motif) ligand 1 (melanoma growth stimulating NM_001511
activity, al ha
CXCL2 Chemokine (C-X-C Motif) Ligand 2 NM 002089
CXCR3 chemokine (C-X-C motif) receptor 3 NM 001504
CYBB cytochrome b-245, beta polypeptide (chronic granulomatous NM_000397
disease)
DPP4 Di e tid I e tidase 4 NM 001935
EGR1 Early growth res onse-1 NM 001964
ELA2 Elastase 2, neutrophil NM 001972
EREG e ire ulin NM 001432
ETS2 v-ets erythroblastosis virus E26 oncogene homolog 2 (avian) NM 005239
F3 coagulation factor III (thromboplastin, tissue factor) NM 001993
FAIM3 Fas a o totic inhibitory molecule 3 NM 005449
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urce M. Lu us Gene Pa el
Gene Ge' ne~Name Ge` n~Ac= c~'es io
S _ . _ol Number
FAS Fas (TNF receptor su erfamil , member 6) NM 000043
FCAR Fc fragment of IgA, receptor for NM 002000
FCGR1A Fc fragment of I G, high affinity receptor IA NM 000566
FCGR2B Fc fragment of I G, low affinity IIb, receptor (CD32) NM 004001
GCLC glutamate-cysteine ligase, catalytic subunit NM 001498
GPR109A G protein-coupled receptor 109A NM 177551
GZMB granzyme B (granzyme 2, cytotoxic T-lymphocyte-associated NM_004131
serine esterase 1)
HLA-DRB1 major histocompatibility complex, class II, DR beta 1 NM 002124
HMGBI hi h-mobilit group box I NM 002128
HMOX1 Heme oxygenase dec clin I NM 002133
HPSI Hermansky-Pudlak syndrome 1 NM 000195
HSPA1A Heat shock protein 70 NM 005345
ICAM1 Intercellular adhesion molecule 1 NM 000201
ICOS inducible T-cell co-stimulator NM 012092
IFI16 Interferon inducible protein 16, gamma NM 005531
IFNA8 interferon, alpha 8 NM 002170
IFNG interferon gamma NM 000619
ILIO interleukin 10 NM 000572
IL12B interieukin 12B (natural killer cell stimulatory factor 2, cytotoxic
NM_002187
I hoc e maturation factor 2, p40)
IL13 Interleukin 13 NM 002188
IL15 Interleukin 15 NM 000585
IL18 Interleukin 18 NM 001562
IL18BP IL-18 Binding Protein NM 005699
IL1A interleukin 1, alpha NM 000575
IL1B Interleukin 1, beta NM 000576
IL1R1 interleukin 1 receptor, type I NM 000877
lLl R2 interleukin 1 receptor, type II NM 004633
lLl RN interleukin 1 receptor antagonist NM 173843
IL2 Interleukin 2 NM 000586
IL32 interleukin 32 NM 004221
IL3RA interleukin 3 rece tor, alpha (low affinity) NM 002183
1L4 interleukin 4 NM 000589
IL5 interieukin 5 colon -stimulatin factor, eosino hil NM 000879
IL6 interleukin 6 (interferon, beta 2) NM 000600
IL6ST interleukin 6 signal transducer (gp130, oncostatin M rece tor NM 002184
IL8 interleukin 8 NM 000584
ISG15 ISG15 ubiguitin-like modifier NM 005101
JAG1 jagged 1 (Alagille s ndrome NM 000214
LCK I m hoc e-s ecific protein tyrosine kinase NM 005356
LGALS3BP lectin, galactoside-binding, soluble, 3 binding protein NM 005567
LTA I m hotoxin alpha (TNF su erfamil , member 1) NM 000595
LY6E I m hoc e antigen 6 complex, locus E NM 002346
MAP3K8 mitogen-activated protein kinase kinase kinase 8 NM 005204
MATK me aka oc e-associated tyrosine kinase NM 002378
MEF2A MADS box transcription enhancer factor 2, polypeptide A NM 005587
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urce M Lu :_ Gene Pa_nel
Gene Gene'Name 'Gene Ac"c ssion
S bol (V, mber
m oc e enhancer factor 2A)
MK167 antigen identified by monoclonal antibody Ki-67 NM 002417
MMP8 matrix metallo e tidase 8 (neutrophil colla enase NM 002424
MMP9 matrix metallopeptidase 9 (gelatinase B, 92kDa gelatinase, NM_004994
92kDa type IV colla enase
MPHOSPH6 M- hase phosphoprotein 6 NM 005792
MPL. ..m elo roliferative leukemia virus oncogene NM 005373
MPZ myelin protein zero (Charcot-Marie-Tooth neuro ath 1 BNM 000530
MT2A Metallothionein 2A NM 005953
MX1 ' Myxovirus resistance 1; interferon inducible protein p78 NM 002462
NFKB1 nuclear factor of kappa light polypeptide gene enhancer in B- NM_003998
cells 1 (p105)
NOS2A nitric oxide synthase 2A (inducible, he atoc es NM 000625
OASL 2'-5'-oli oaden late synthetase-like NM 003733
PLA2G7 phospholipase A2, group VII (platelet-activating factor NM_005084
ace Ih drolase,. plasma)
PLAU plasminogen activator, urokinase NM 002658
PLAUR plasminogen activator, urokinase receptor NM 002659
PLSCR1 phospholipid scramblase NM 021105
PTGS2 prostaglandin-endoperoxide synthase 2(prostagiandin G/H NM_000963
synthase and c cloox enase
PTPNI protein tyrosine Ohos hatase, non-receptor type 1 NM 002827
PTPRC protein tyrosine phosphatase, receptor type, C NM 002838
P.TX3 Pentaxin Related Gene, Ra idl Induced by IL-lb NM 002852
RAB27A RAB27A, member RAS oncogene family NM 173235
RGSI regulator of G-protein si nallin 1 NM 002922
RNASE2 ribonuclease, RNase A family, 2 (liver, eosinophil-derived NM_002934
neurotoxin)
SELE selectin E (endothelial adhesion molecule 1) NM 000450
SERPINAI serpin peptidase inhibitor, clade A(alpha-1 antiproteinase,
NM_001002235
antit sin , member 1
SERPINEI serpin peptidase inhibitor, clade E (nexin, plasminogen activator
NM_000602
inhibitor type 1), member 1
SERPING1 serpin peptidase inhibitor, clade G(C1 inhibitor), member 1,
NM_000062
(angioedema, hereditary)
SGK serum/glucocorticoid regulated kinase NM 005627
SOD2 superoxide dismutase 2, mitochondrial NM 000636
SSB Sjogren syndrome antigen B (autoantigen La) NM 003142
TGFB1 transforming growth factor, beta 1(Camurati-Engelmann NM_000660
disease)
THBSI thrombospondin 1 NM 003246
TIMPI tissue inhibitor of metalloproteinase 1 NM 003254
TLR2 toll-like receptor 2 NM 003264
TLR3 toll-like receptor 3 NM 003265
TLR4 toll-like receptor 4 NM 003266
TLR7 toll-like receptor 7 NM 016562
TLR9 toll-like receptor 9 NM 017442
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Sour "e AliO ay us Ge_ne Pa~ne)
Gene G ne'tPla e G e Acc ; sion
S ~_bol. N~mber ~
TNF tumor necrosis factor (TNF su erfamil , member 2) NM 000594
TNFSFIO tumor necrosis factor li and su erfamil , member 10 NM 003810
TNFSF13B Tumor necrosis factor li and su erfamil , member 13b NM 006573
TP53 tumor protein p53 (Li-Fraumeni s drome NM 000546
TRIM21 tripartite motif-containing 21 NM 003141
TRIM25 tripartite motif-containing 25 NM 005082
TROVE2 TROVE domain family, member 2 NM 004600
TXNRD1 thioredoxin reductase NM 003330
USP20 ubi uitin specific peptidase 20 NM 006676
VEGF vascular endothelial growth factor NM 003376
TABLE 2: Precision Profile"m for Inflanunatory Response
Gene ~~ T ene Accessio
Nu b~e
ADAM17 a disintegrin and metalloproteinase domain 17 (tumor NM_003183
necrosis factor, alpha, converting en e
ALOX5 arachidonate 5-lipoxygenase NM_000698
ANXA11 annexin A11 NM_001157
APAF1 apoptotic Protease Activating Factor 1 NM_013229
BAX BCL2-associated X protein .. NM_138761
C1QA complement component 1, q subcomponent, alpha NM_015991
polypeptide
CASP1 caspase 1, apoptosis-related cysteine peptidase (interleukin NM_033292
1, beta, convertase
CASP3 caspase 3, apoptosis-related cysteine peptidase NM_004346
CCL2 chemokine (C-C motif) ligand 2 NM_002982
CCL3 chemokine (C-C motif) ligand 3 NM_002983
CCL5 chemokine (C-C motif) ligand 5 NM_002985
CCR3 chemokine (C-C motif) receptor 3 NM_001837
CCR5 chemokine (C-C motif) receptor 5 NM_000579
CD14 CD14 antigen NM_000591
CD19 CD19 Antigen NM_001770
CD4 CD4 antigen (p55) NM_000616
CD86 CD86 antigen (CD28 antigen ligand 2, B7-2 antigen) NM_006889
CD8A CD8 antigen, alpha polypeptide NM_001768
CRP C-reactive protein, pentraxin-related NM_000567
CSF2 colony stimulating factor 2 (granulocyte-macrophage) NM_000758
CSF3 colony stimulating factor 3 (granulocytes) NM_000759
CTLA4 cytotoxic T-lymphocyte-associated protein 4 NM_005214
CXCL1 chemokine (C-X-C motif) ligand 1(melanoma growth NM_001511
stimulating activity, al ha
CXCL10 chemokine (C-X-C moif) ligand 10 NM_001565
CXCL3 chemokine (C-X-C motif) ligand 3 NM 002090
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Gene Ge jeffime T~ (aene Ac'~cessio B SEE 0 0 S, mbol Number
CXCL5 chemokine (C-X-C motif) ligand 5 NM_002994
CXCR3 chemokine (C-X-C motif) receptor 3 NM_001504
DPP4 Dipeptidylpeptidase 4 NM_001935
EGRI early growth response-1 NM_001964
ELA2 elastase 2, neutrophil NM_001972
FAIM3 Fas apoptotic inhibitory molecule 3 NM_005449
FASLG Fas ligand (TNF superfamily, member 6) NM_000639
GCLC glutamate-cysteine ligase, catalytic subunit NM_001498
GZMB granzyme B (granzyme 2, cytotoxic T-lymphocyte-associated NM_004131
serine esterase 1)
HLA-DRA major histocompatibility complex, class II, DR alpha NM_019111
HMGBI high-mobility group box 1 NM_002128
HMOX1 heme oxygenase (decycling) 1 NM_002133
HSPA1A heat shock protein 70 NM_005345
ICAM1 Intercellular adhesion molecule 1 NM_000201
ICOS inducible T-cell co-stimulator NM012092.
1FI16 interferon inducible protein 16, gamma NM_005531
IFNG interferon gamma NM_000619
ILIO interleukin 10 NM_000572
IL12B interleukin 12 p40 NM_002187
IL13 interleukin 13 NM_002188.
IL15 Interleukin 15 NM_000585
IRFI interferon regulatory factor 1 NM_002198
IL18 interieukin 18 NM_001562
IL18BP IL-18 Binding Protein NM_005699
IL1A interleukin 1, alpha NM_000575.
ILIB interleukin 1, beta NM_000576
IL1R1 interleukin 1 receptor, type I NM_000877
IL1RN interleukin 1 receptor antagonist NM_173843
IL2 interleukin 2 NM_000586
IL23A interleukin 23, alpha subunit p19 NM_016584
IL32 interleukin 32 NM_001012631
IL4 interleukin 4 NM_000589
IL5 interleukin 5 (colony-stimulating factor, eosinophil) NM_000879
IL6 interleukin 6 (interferon, beta 2) NM000600
IL8 interleukin 8 NM_000584
LTA lymphotoxin alpha (TNF superfamily, member 1) NM_000595
MAP3KI mitogen-activated protein kinase kinase kinase 1 XM_042066
MAPK14 mitogen-activated protein kinase 14 NM_001315
MHC2TA class II, major histocompatibility complex, transactivator NM_000246
MIF macrophage migration inhibitory factor (glycosylation- NM_002415
inhibiting factor)
MMP12 matrix metallopeptidase 12 (macrophage elastase) NM_002426
MMP8 matrix metallopeptidase 8 (neutrophil collagenase) NM 002424
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Gene Gene ame Gene Af e~'ce sion
; bol Number
MMP9 matrix metallopeptidase 9 (gelatinase B, 92kDa gelatinase, NM_004994
92kDa type IV colla enase
MNDA myeloid cell nuclear differentiation antigen NM_002432
MPO myeloperoxidase NM_000250
MYC v-myc myelocytomatosis viral oncogene homolog (avian) NM_002467
NFKB1 nuclear factor of kappa light polypeptide gene enhancer in B- NM_003998
cells 1 105
NOS2A nitric oxide synthase 2A (inducible, hepatocytes) NM_000625
PLA2G2A phospholipase A2, group IIA (platelets, synovial fluid) NM_000300
PLA2G7 phospholipase A2, group VII (platelet-activating factor NM_005084
ace Ih drolase, plasma)
PLAU plasminogen activator, urokinase NM_002658
PLAUR plasminogen activator, urokinase receptor NM_002659
PRTN3 proteinase 3 (serine proteinase, neutrophil, Wegener NM_002777
granulomatosis autoanti en
PTGS2 prostaglandin-endoperoxide synthase 2 (prostaglandin G/H NM_000963
synthase and c cloox enase
PTPRC protein tyrosine phosphatase, receptor type, C NM_002838
PTX3 pentraxin-related gene,rapidly induced by IL-1 beta NM_002852
SERPINAI serine (or cysteine) proteinase inhibitor, clade A(alpha-1 NM_000295
antiproteinase, antit sin , member 1
SERPINE1 serpin peptidase inhibitor, clade E (nexin, plasminogen NM_000602.
activator inhibitor type 1), member 1
SSI-3 : suppressor of cytokine signaling 3 NM_003955
TGFB1 transforming growth factor, beta 1(Camurati-Engelmann NM_000660
disease)
TIMP1 tissue inhibitor of metalloproteinase I NM_003254
TLR2 toll-like receptor 2 NM_003264
TLR4 toll-like receptor 4 NM_003266
TNF tumor necrosis factor (TNF superfamily, member 2) NM000594
TNFRSF13B tumor necrosis factor receptor superfamily, member 13B NM_012452
TNFRSF17 tumor necrosis factor receptor superfamily, member 17 NM_001192
TNFRSFIA tumor necrosis factor receptor superfamily, member 1A NM_001065
TNFSF13B Tumor necrosis factor (ligand) superfamily, member 13b NM_006573
TNFSF5 CD40 ligand (TNF superfamily, member 5, hyper-IgM NM_000074
s ndrome
TXNRDI thioredoxin reductase NM_003330
VEGF vascular endothelial growth factor NM 003376
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TABLE 3: Normal and HV v. DLE, SCLE, and LET: Ranldng of p-value genes from
Table 1 from most to least
significant: GOLDMineR Ordinal Logit Model (interval scale with optimal scores
estimated for each group
individually)
Normal
Cluster4
Cluster Size 0.5000
gene N= 50 ordinal
id# p-value
35 $jERPI 19.37
17 IFt6 16.38 7 -
32 ! ~lS 18.14 5= 1'6
33 P ~SCR 17.00 6.2 =
29 18.88 2 = '14
7 0; CL2 ll'1' 24.30 i#15 "
46 17.41
38 jIlAW 19.12 E~-08
31 NFKB1 17.29 1.3E-07
3 CALR 14.45 2.0E-06
9 CCR10 21.76 2.7E-05
28 IL6ST 17.90 0.00021
16 ICAM1 17.79 0.00053
19 IL10 23.25 0.00062
27 I1-6 25.14 0.0013
13 FCAR 16.78 0.0035
14 FCGRIA 17.56 Ø0046.
45 TNFSF5 18.06 0.0053
23 IL1 B 16.44 0.0094
1 BST1 16.06 0.012
47 TROVE2 17.46 0.015
36 SGK 17.25 0.017
CD68 14.21 0.022
FCGR2B 12.38 0.025
24 IL32 14.08 0.029
11 CR1 17.23 0.059
34 MMP9 15.78 0.061
37 SSB 18.67 0.081
2 C1 QA 20.77 0.084
IL12B 25.78 0.085
40 TLR4 15.18 0.1
26 IL4 24.78 0.12
5 CCL17 25.85 0.16
6 CCL19 25.74 0.18
12 CXCR3 17.94 0.18
8 CCL24 25.82 0.21
IL15 21.16 0.25
22 TNFRSF6 16.64 0.26
SELE 25.74 0.35
21 IL3RA 20.29 0.36
44 IL18 21.38 0.38
4 CASP3 20.41 0.48
48 VEGF 23.28 0.49
43 TNFRSF5 19.36 0.53
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Normal
Cluster4
Cluster Size 0.5000
gene N= 50 ordinal
id# p-value
39 TLR3 23.31 0.61
42 TNF 18.64 0.64
41 TLR9 17.70 0.7
18 IFNG 23.18 0.8
TABLE 4: Normal (excluding HV) v. Lupus (DLE, SCLE, LET): Ranking of genes
based on Table 1 from most to
least significant: Stepwise logistic regression analysis (group membership
(i.e., Normal v. lupus is predicted as a
function of ene ex ression
LG
STEP p-value R-S uare
U, G7A LS3BI' =s 1 E',..1 0.531
IFI6 1 5.6E-14
OASL 1 9.IE-14
PLSCRI 1 6.OE-12
SERPING1 1 9.9E-12
TRIM21 1 7.8E-11
THBS1 6.4E-10
CCL2 2.3E-09
NFKB1 3.4E-08
CALR 1 4.3E-08
ICAM1 1 3.OE-05
IL6ST 1 0.00016
CCRIO 1 0.00023
FCAR 1 0.00092
BST1 1 0.0025
FCGR2B 1 0.0029
FCGRIA 1 0.0035
IL32 1 0.0038
CD68 1 0.0048
SGK 1 0.0049
CR1 1 0.0059
IL1 B 1 0.0061
IL6 1 0.0078
IL4 1 0.011
TLR4 1 0.017
TROVE2 1 0.019
CXCR3 1 0.036
TNFRSF6 1 0.046
SSB 1 0.14
MMP9 1 0.15
SELE 1 0.17
C1QA 1 0.18
IL15 1 0.28
TLR9 1 0.31
IL18 1 0.36
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LG
STEP p-value R-S uare
TNFSF5 1 0.56
CASP3 1 0.57
VEGF 1 0.62
IFNG 1 0.69
CCL17 1 0.71
IL3RA 1 0.72
TLR3 1 0.78
TNFRSF5 1 0.83
CCL24 1 0.87
TNF 1 0.89
IL10 1 0.91
IL12B 1 0.98
CCL19 1 0.98
TABLE 5: Non-Lupus (Normal and HV) v. Lupus (DLE, SCLE and LET) - Ranking of
genes based on Table 1
from most to least significant: Stepwise logistic regression analysis (group
membership (i.e., non-lupus v. lupus is
predicted as a function of gene e ression
LG
STEP p-value R-S uare
LGAt~S B 1 2E- ,5 0.517
IFI6 1 5.3E-15
OASL 1 8.2E-15
PLSCR1 I 5.8E-13
SERPING1 1 1.3E-12
CCL2 I 5.6E-1 1
TRIM21 1 4.8E-10
THBS1 1 2.8E-08
CALR 1 7.7E-08
NFKB1 1 6.1 E-06
ICAM 1 1 0.00015
CCR10 1 0.00023
FCAR 1 0.00089
IL6ST 1 0.00094
FCGR1A 1 0.0015
CD68 1 0.0016
SGK 1 0.0027
BST1 1 0.0035
I L6 1 0.0042
I L32 1 0.0043
FCGR2B 1 0.0051
IL4 1 0.009
IL1B 1 0.012
TLR4 1 0.019
CR1 1 0.019
CXCR3 1 0.038
TROVE2 1 0.051
C1QA 1 0.063
TNFRSF6 1 0.07
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LG
STEP p-value R-S uare
SSB 1 0.1
IL15 1 0.19
MMP9 1 0.25
TLR9 1 0.27
IL10 1 0.39
IL18 1 0.45
VEGF 1 0.5
TNFRSF5 1 0.56
SELE 1 0.57
IL12B 1 0.58
CCL24 1 0.58
TLR3 1 0.63
CASP3 1 0.63
IFNG 1 0.63
CCL19 1 0.66
TNFSF5 1 0.71
CCL17 1 0.83
TNF 1 0.94
IL3RA 1 0.95
TABLE 6: Normal (excluding HV) v. Lupus (DLE, SCLE, LET): Ranking of genes
based on Table 1 from most to
least si ificant: Stepwise re ession analysis after 2 steps of stepwise
regression)
LG
STEP -value R-S uare
GALS3BP , fi~ x;1~3Et1,~4 0.531
SGK ~"--3 0.616
THBS1 2 4.4E-03
TNFRSF5 2 0.0056
IFI6 2 0.0065
OASL 2 0.0091
TNF 2 0.018
FCGR1A 2 0.051
SERPING1 2 0.084
CCL2 2 0.099
C1QA 2 0.12
TLR9 2 0.14
PLSCR1 2 0.17
SELE 2 0.19
TNFSF5 2 0.24
TRIM21 2 0.24
VEGF 2 0.24
CCR10 2 0.25
CCL19 2 0.26
IL10 2 0.26
FCGR2B 2 0.29
SSB 2 0.3
IL6 2 0.31
CD68 2 0.31
TLR4 2 0.32
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LG
STEP p-value RS uare
I CAM 1 2 0.35
CALR 2 0.37
IL15 2 0.39
IL1 B 2 0.4
NFKB1 2 0.42
IL4 2 0.43
BSTI 2 0.43
CR1 2 0.44
IL18 2 0.53
IL3RA 2 0.56
IL6ST 2 0.56
MMP9 2 0.59
IFNG 2 0.6
I L32 2 0.72
FCAR 2 0.73
TROVE2 2 0.74
TLR3 2 0.82
CASP3 2 0.87
IL12B 2 0.95
CCL24 2 0.96
CCL17 2 0.96
TNFRSF6 2 1
CXCR3 2 1
TABLE 7: Non-Lupus (Normal and HV) v. Lupus (DLE, SCLE and LET) - Ranking of
genes based on Table 1
from most to least significant: Ste wise re ession analysis (after 2 steps of
stepwise regression)
LG
STEP -value R-S uare
LGAL 3 ';1' 1~-11'3 2E-15 0.517
SGK;'rs~ 4 tr2;0.0008= 0.611
IF16 2 0.0025
OASL 2 0.0039
TNFRSF5 2 0.0069
CCL2 2 0.022
THBS1 2 0.027
SERPING1 2 0.035
TNF 2 0.041
PLSCR1 2 0.059
CCR10 2 0.13
TNFSF5 2 0.14
TLR9 2 0.14
IL3RA 2 0.15
FCGRIA 2 0.22
IL6ST 2 0.22
VEGF 2 0.26
SSB 2 0.28
IL6 2 0.29
CR1 2 0.3
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LG
STEP p-value RS uare
FCGR2B 2 0.32
ICAM1 2 0.33
IL4 2 0.35
IL1B 2 0.37
TROVE2 2 0.4
IFNG 2 0.41
C 1 QA 2 0.41
TLR4 2 0.41
MMP9 2 0.42
CD68 2 0.43
BST1 2 0.43
TRIM21 2 0.44
IL18 2 0.46
IL12B 2 0.46
IL15 2 0.54
CALR 2 0.55
CCL19 2 0.66
IL32 2 0.66
CCL17 2 0.66
TLR3 2 0.67
CCL24 2 0.69
SELE 2 0.74
FCAR 2 0.78
TNFRSF6 2 0.83
NFKB1 2 0.85
IL10 2 0.86
CASP3 2 0.88
CXCR3 2 0.97
TABLE 8: Classification Rates for 2-Gene Model LGALS3BP and SGK
Normals 97%
DLE 81%
SCLE 91%
LET 54%
TABLE 9: Normal and HV v. DLE, SCLE and LET- Ranking of genes based on Table 1
from most to least
significant: GOLDMineR Ordinal Logit Model (custom 2-gene model based on
interval scale with optimal scores
estimated for each group individuall
LG
STEP p-value R-S uare
iFl6 1 .7E-16~ 0.710
THBS1 2~ 3~2E',~05~
LGALS3BP 2 0.0042
NFKB1 2 0.0072
I L6 2 0.0073
CALR 2 0.011
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LG
STEP p-value R-S uare
IL3RA 2 0.015
SERPING1 2 0.016
IL4 2 0.082
FCGR2B 2 0.1
FCGR1A 2 0.11
C1QA 2 0.12
SSB 2 0.12
TNFSF5 2 0.15
CXCR3 2 0.19
CCR10 2 0.19
CCL17 2 0.22
IL12B 2 0.25
SGK 2 0.25
TNFRSF6 2 0.25
TLR3 2 0.25
CCL24 2 0.27
SELE 2 0.3
TNFRSF5 2 0.3
TLR4 2 0.33
TROVE2 2 0.33
TRIM21 2 0.35
TLR9 2 0.35
IL15 2 0.37
IL32 2 0.38
IL1B 2 0.39
IL18 2 0.42
IL6ST 2 0.44
PLSCRI 2 0.46
VEGF 2 0.47
FCAR 2 0.5
IL10 2 0.56
OASL 2 0.59
TNF 2 0.64
BST1 2 0.65
ICAM1 2 0.74
IFNG 2 0.75
CR1 2 0.75
CCL19 2 0.83
CASP3 2 0.84
CD68 2 0.89
MMP9 2 0.9
CCL2 2 0.93
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TABLE 10: Combined Normal/ HV v. Combined DLE/ SCLE (Ordinal Fixed where SCLE
and DLE = 1, vs HV
and normals = 0: Ranking of genes based on Table I from most to least
significant: Stepwise regression analysis
LG
STEP p-value R-S uare R-S uare
C? S 2.QE 8 0.7659 0.772
SERPING1 1 2.8E-18
IF16 1 6.5E-18
PLSCRI 1 9.0E-17
LGALS3BP I 2.6E-14
CCL2 I 1.2E-12
TRIM21 1 1.5E-10
THBS1 3.7E-08
CALR 1 3.OE-06
FCAR 1 0.00011
ICAM1 1 0.00017
FCGR1A 1 0.00027
IL1 B 1 0.00084
NFKB1 1 0.00086
BST1 0.00093
SGK 0.001
FCGR2B 1 0.0024
CD68 0.0032
TLR4 1 0.0081
CCR10 1 0.009
C1 QA 1 0.023
CR1 1 0.023
IL6 1 0.029
I L4 1 0.04
IL32 1 0.047
IL6ST 1 0.054
TNFRSF6 1 0.12
MMP9 1 0.13
IL12B 1 0.14
IL15 1 0.18
TNFSF5 1 0.18
CXCR3 1 0.23
CCL19 1 0.25
TROVE2 1 0.33
TLR3 1 0.42
IL3RA 0.42
CCL17 0.54
CCL24 0.54
TLR9 1 0.62
IFNG 1 0.64
SSB 1 0.64
IL18 1 0.66
TNF 0.7
IL10 1 0.75
SELE 1 0.79
VEGF 1 0.97
TNFRSFS 1 0.98
CASP3 1 0.99
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TABLE 11: Normal and HV v. DLE and SCLE (where each of the four groups are
considered distinctly, model
based on ordinal logit (Normal, HV, SCLE, DLE)) - Ranking of genes based on
Table 1 from most to least
significant: St wise re ession anal sis
LG
STEP p-value RS uare R-S uare
~ASL ' 6 0.768
SERPING1 I 1.2E-16
IF16 1 4.0E-16
PLSCRI 1 5.8E-15
LGALS3BP 1 7.6E-13
CCL2 1 6.5E-12
TRIM21 1 4.OE-10
THBS1 1 8.7E-09
NFKB1 1 1.3E-05
CALR 1 1.8E-05
ICAM 1 1 0.00027
FCAR 1 0.0012
FCGR1A 1 0.0025
CCRIO 1 0.0038
IL1 B 1 0.0041
BST1 1 0.0058
SGK 1 0.007
FCGR2B 1 0.0094
IL6 0.013
CD68 0.019
IL10 0.021
IL6ST 0.028
CR1 0.039
SELE 1 0.043
C 1 QA 0.05
TLR4 0.053
CCL24 0.058
TROVE2 1 0.095
IL12B 0.096
CCL17 1 0.13
IL32 1 0.19
MMP9 1 0.2
IL18 1 0.22
CCL19 1 0.23
IL4 1 0.23
IL15 1 0.26
IL3RA 1 0.3
TNFRSF6 1 0.35
TNFSF5 1 0.39
CASP3 1 0.52
TLR3 1 0.53
CXCR3 1 0.59
TNFRSF5 1 0.62
IFNG 1 0.65
TNF 1 0.66
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LG
STEP p-value R-S uare R-S uare
VEGF 1 0.78
SSB 1 0.8
TLR9 1 0.97
TABLE 12: Combined Normal/ HV v. Combined DLE/ SCLE (Ordinal Fixed where SCLE
and DLE = 1, vs HV
and normals = 0): Ranking of genes based on Table 1 from most to least
significant: Stepwise regression analysis
(after 2 steps of stei) wise re ession
LG
STEP p-value R-Square R-Square
fJ~A~SL 2 0.7659 0.772
2 0~ 1r 0.8068 0.813
CCL17 2 0.044
IL3RA 2 0.052
TNFRSF5 2 0.066
IL12B 2 0.13
SERPING1 2 0.15
THBS1 2 0.15
CALR 2 0.17
IL4 2 0.17
FCGR2B 2 0.19
SGK 2 0.21
CXCR3 2 0.23
LGALS3BP 2 0.27
TNFSF5 2 0.27
C 1 QA 2 0.28
CCL24 2 0.3
FCGRIA 2 0.3
SELE 2 0.33
SSB 2 0.38
TLR4 2 0.4
TROVE2 2 0.42
TNF 2 0.42
CD68 2 0.51
CCL19 2 0.52
BST1 2 0.52
I L32 2 0.53
IL1 B 2 0.53
TNFRSF6 2 0.55
IL6ST 2 0.56
TLR3 2 0.58
IL18 2 0.61
IL15 2 0.61
IF16 2 0.62
VEGF 2 0.62
CR1 2 0.62
NFKB1 2 0.66
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LG
STEP p-value R-Square R-Square
TLR9 2 0.75
IFNG 2 0.81
CCL2 2 0.82
IL10 2 0.82
FCAR 2 0.82
ICAM1 2 0.85
CCR10 2 0.86
PLSCRI 2 0.87
MMP9 2 0.88
CASP3 2 0.97
TRIM21 2 1
TABLE 13: Normal and HV v. DLE and SCLE (where each of the four groups are
considered distinctly, model
based on ordinal logit (Normal, HV, SCLE, DLE) - Ranking of genes based on
Table 1 from most to least
significant: Ste wise re ession anal sis (after 2 ste s of stepwise
regression)
STEP p-value R-S uare R-S uare
OASL ,1 =- 6 =_~0~7L 0.768
Tl B- 1 2 (}.0 0~77 0.790
IL6 2 0.015
TNFRSF5 2 0.018
CCL17 2 0.026
IL3RA 2 0.039
CALR 2 0.071
C1QA 2 0.100
IL12B 2 0.110
SERPINGI 2 0.110
FCGRIA 2 0.15
IL4 2 0.17
FCGR2B 2 0.18
LGALS3BP 2 0.2
CXCR3 2 0.21
SGK 2 0.23
IL10 2 0.25
TNF 2 0.26
CD68 2 0.33
TNFSF5 2 0.33
CCL24 2 0.34
SSB 2 0.35
SELE 2 0.42
I L32 2 0.43
TLR4 2 0.46
IL18 2 0.46
VEGF 2 0.52
IL15 2 0.55
TRIM21 2 0.56
TROVE2 2 0.56
TNFRSF6 2 0.61
IL1 B 2 0.63
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STEP p-value R-S uare R-S uare
NFKB1 2 0.63
BST1 2 0.67
TLR3 2 0.67
CCL19 2 0.68
TLR9 2 0.7
IFNG 2 0.7
MMP9 2 0.72
CCL2 2 0.77
CCR10 2 0.79
IL6ST 2 0.81
CR1 2 0.82
ICAM1 2 0.83
IFI6 2 0.85
FCAR 2 0.92
CASP3 2 0.93
PLSCR1 2 0.95
TABLE 14: Classification Rates for 2-Gene Model OASL and THBS1
Normals 98%
DLE 88%
SCLE 91%
TABLE 15: LET vs. Normal and HV: Ranking of genes based on Table 1 from most
to least significant: Stepwise
regression analysis
LG
STEP p-value R-S uare
E_~c~.WS3B_ P ~ 2 OE~;o6~i ~1~9~
IL6ST 1 5.2E-05
NFKBI 1 8.OE-05
CALR 1 8.9E-05
CCR10 1 0.00022
TNFSF5 1 0.0013
IF16 1 0.0019
THBS1 1 0.0019
OASL 1 0.0021
IL32 1 0.0025
TRIM21 1 0.0027
SSB 1 0.0031
TROVE2 1 0.0045
CCL2 1 0.005
PLSCR1 1 0.011
IL6 1 0.013
CXCR3 1 0.027
IL4 1 0.031
ICAM1 1 0.055
SERPING1 1 0.064
CD68 1 0.081
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STEP p-value R-S uare
VEGF 1 0.12
TLR9 1 0.17
IL10 1 0.17
TNFRSF6 1 0.2
CR1 1 0.23
IL12B 1 0.23
CASP3 1 0.24
TNFRSF5 1 0.24
FCGR2B 1 0.27
IL3RA 1 0.29
SGK 1 0.29
CCL19 1 0.33
IL18 1 0.33
FCAR 1 0.38
TNF 1 0.39
BST1 1 0.42
SELE 1 0.45
TLR4 1 0.48
FCGR1A 1 0.52
CCL17 1 0.56
IL15 1 0.56
TLR3 1 0.68
CCL24 1 0.76
IFNG 1 0.81
C 1 QA 1 0.86
MMP9 1 0.93
IL1 B 1 0.96
TABLE 16: LET vs. Normal and HV: Ranking of genes based on Table 1 from most
to least significant: Stepwise
re ession anal ysis after 2 ste s of st wise re ession)
LG
STEP p-value RSquare R-Square
TMAiL~S3BP 13 ~Q -06! QT346 U~319
C~R"tT0 < 0. 0 ~7 U.51~2
SGK 2 0.02
THBS1 2 0.059
IL1B 2 0.086
TNF 2 0.12
MMP9 2 0.13
CCL19 2 0.14
FCGRIA 2 0.15
FCGR2B 2 0.19
SSB 2 0.24
SELE 2 0.24
TLR4 2 0.27
CALR 2 0.27
IL4 2 0.28
I L6 2 0.28
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LG
STEP p-value R-Square R-Square
CCL2 2 0.29
NFKB1 2 0.31
IL12B 2 0.31
TNFRSF5 2 0.32
CR1 2 0.32
BST1 2 0.37
CCL17 2 0.37
SERPINGI 2 0.37
IL6ST 2 0.39
IL32 2 0.4
ICAM1 2 0.4
CXCR3 2 0.41
TLR9 2 0.46
IFNG 2 0.46
C1QA 2 0.46
FCAR 2 0.5
CASP3 2 0.54
TRIM21 2 0.54
IL18 2 0.55
TNFRSF6 2 0.56
IF16 2 0.57
TLR3 2 0.59
VEGF 2 0.62
TNFSF5 2 0.64
CD68 2 0.66
TROVE2 2 0.7
IL3RA 2 0.75
IL15 2 0.75
OASL 2 0.76
PLSCR1 2 0.85
CCL24 2 0.92
IL10 2 0.93
TABLE 17: 2-gene models that correctly distinguish between DLE/SCLE vs.
Normals/HV each with at least 75%
accuracy
LG
Gene 1 Gene 2 Correct Classification
Incremental Incremental
p-value p-value
gene 1 gene 2 DLE - SCLE HV - Normals R-SQ
SERPING1 FCGRIA 1.1E-05 0.04 96% 95% 0.828
PLSCR1 FCGR2B 1.6E-05 0.022 89% 97% 0.776
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Gene I Gene 2 Correct Classification
Incremental Incremental
p-value p-value
gene I gene 2 DLE - SCLE HV - Normals R-SQ
PLSCRI TNFRSF5 2.8E-05 0.039 89% 93% 0.748
PLSCR1 SGK 3.8E-06 0.041 89% 95% 0.764
LGALS3BP TNFRSF5 5.9E-05 0.0034 85% 97% 0.741
LGALS3BP SGK 3.3E-06 0.011 85% 97% 0.703
LGALS3BP 1.5E-03 0.012 81% 100% 0.714
LGALS3BP IL6ST 6.3E-06 0.021 81% 98% 0.673
LGALS3BP SSB 7.3E-06 0.02 81% 97% 0.676
LGALS3BP TNFSF5 6.6E-06 0.033 81% 98% 0.668
LGALS3BP 1L3RA 3.3E-06 0.038 78% 100% 0.666
1TRIM21 4.7E-04 0.0067 81% 97% 0.637
THBS1 7.1 E-05 0.0056 85% 93% 0.621
SGK 4.4E-06 0.017 85% 95% 0.633
TNF 1.3E-06 0.028 85% 97% 0.633
TNFRSF5 2.6E-06 0.038 81% 95% 0.596
TRIM21 SGK 4.3E-06 3.10E-04 93% 93% 0.714
TRIM21 TROVE2 1.4E-05 0.0041 78% 87% 0.541
TRIM21 TLR4 2.8E-06 0.003 78% 88% 0.530
TRIM21 NFKB1 1.3E-05 0.0031 81% 85% 0.525
TRIM21 1L3RA 4.OE-06 0.0046 81% 85% 0.518
TRIM21 CR1 2.8E-06 0.0066 78% 92% 0.522
TRIM21 TNFSF5 5.5E-06 0.0086 78% 82% 0.491
TRIM21 FCGR2B 8.OE-06 0.0073 78% 87% 0.496
TRIM21 VEGF 7.5E-06 0.014 81% 82% 0.486
TRIM21 BST1 9.1 E-06 0.011 81% 85% 0.498
TRIM21 TNFRSF6 7.8E-06 0.016 81% 85% 0.487
TRIM21 TLR9 2.1 E-06 0.017 85% 82% 0.487
TRIM21 IL18 2.9E-06 0.023 81 % 83% 0.491
TRIM21 THBS1 6.9E-04 0.019 85% 83% 0.475
TRIM21 TNF 8.4E-06 0.026 85% 82% 0.461
TRIM21 TLR3 9.OE-06 0.029 81% 85% 0.472
TRIM21 MMP9 3.OE-06 0.036 81% 85% 0.492
TRIM21 ICAM1 1.8E-05 0.039 78% 85% 0.458
THBS1 SGK 6.5E-06 9.OOE-04 81% 85% 0.478
THBS1 FCGRIA 3.2E-05 0.0031 78% 77% 0.422
THBS1 CALR 4.4E-04 0.012 78% 83% 0.412
THBS1 IL3RA 1.2E-05 0.023 85% 80% 0.376
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TABLE 18: 3-gene models that correctly distinguish between DLE/SCLE vs.
Normals/HV each with at least 75%
accuracy
LG
Gene 1 Gene 2 Gene 3 Correct
Incremental Incremental Incremental Classification
p-value p-value p-value
gene 1 gene 2 gene 3 DLE - HV - R-SQ
SCLE Normals
PLSCR1 FCGR2B TNFRSF5 3.6E-04 0.0140 0.0320 96% 98% 0.842
PLSCR1 TNFRSF5 LGALS3BP 2.3E-04 0.0098 0.0400 93% 97% 0.795
PLSCR1 TNFRSFS CALR 0.0089 89% 100%
PLSCR1 TNFRSF5 FCAR 1.8E-04 0.0340 0.0480 89% 98% 0.791
LGALS3BP TNFRSF5 IF16 0.0240 0.0190 0.01200 89% 98% 0.834
LGALS3BP TNFRSF5 OASL 0.0360 0.0250 0.00800 96% 97% 0.838
LGALS3BP TNFRSFS SERPING1 0.0410 0.0400 0.0110 85% 100% 0.811
LGALS3BP TNFRSF5 0.0026 0.0083 0.0210 89% 100% 0.805
LGALS3BP SGK MMP9 2.4E-05 0.0025 0.0260 81% 100% 0.739
LGALS3BP SGK THBS1 2.5E-04 0.0071 0.0470 85% 98% 0.725
LGALS3BP SSB 0.0013 0.0130 0.018 81% 100% 0.761
LGALS3BP TNF 0.0014 0.0036 0.042 89% 100% 0.768
LGALS3BP IL6ST TRIM21 5.3E-04 0.0098 0.042 93% 95% 0.716
LGALS3BP SSB IFNG 1.4E-04 0.0064 0.027 89% 95% 0.723
LGALS3BP TNFSF5 1.6E-03 0.0450 0.015 89% 98% 0.751
LGALS3BP IL3RA TRIM21 1.8E-03 0.0180 0.035 93% 95% 0.721
LGALS3BP IL3RA THBS1 1.3E-04 0.0220 0.046 78% 100% 0.699
TRIM21 CD68 8.2E-05 0.0008 0.013 89% 95% 0.691
TRIM21 TNF 2.7E-04 0.0033 0.019 89% 97% 0.715
TRIM21 TNFRSF5 3.1 E-04 0.0033 0.02 78% 98% 0.678
TRIM21 IL3RA 6.7E-04 0.0017 0.016 81% 100% 0.710
TRIM21 FCGR2B 1.2E-03 0.0019 0.036 78% 97% 0.669
TRIM21 SSB 3.7E-04 0.0028 0.044 81% 98% 0.679
THBS1 SGK 1.7E-04 0.0025 0.007 89% 98% 0.731
THBSI IL3RA 6.4E-05 0.0026 0.024 81% 98% 0.676
SGK CR1 7.1 E-05 0.0019 0.0044 85% 100% 0.735
SGK MMP9 2.3E-05 0.0016 0.0042 93% 97% 0.755
SGK FCAR 6.5E-04 0.0025 0.011 89% 98% 0.752
SGK IL1B 1.9E-04 0.0039 0.013 89% 98% 0.713
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Gene I Gene 2 Gene 3 Correct
Incremental Incremental Incremental Classification
p-value p-value p-value
gene I gene 2 gene 3 DLE - HV - R-SQ
SCLE Normals
SGK BST1 9.4E-04 0.0028 0.019 85% 98% 0.707
SGK ICAM1 2.7E-04 0.0036 0.021 89% 97% 0.699
SGK TNFRSF5 6.5E-06 0.0160 0.035 85% 97% 0.683
SGK TLR4 5.OE-03 7.8E-05 0.029 85% 100% 0.684
SGK NFKB1 2.6E-04 0.0056 0.032 85% 98% 0.686
SGK FCGR2B 1.3E-04 0.0048 0.045 89% 97% 0.689
TNF CALR 4.7E-05 0.0037 0.019 85% 98% 0.713
TNF IL1 B 3.6E-08 0.0200 0.039 81% 98% 0.670
TNF NFKB1 4.4E-08 0.0120 0.043 85% 97% 0.672
TNF CXCR3 1.4E-06 0.0041 0.025 81% 98% 0.661
TNFRSF5 CALR 6.80E-05 0.0020 0.0036 81% 97% 0.692
TNFRSF5 FCGRIA 8.70E-06 0.0150 0.026 81% 97% 0.635
TNFRSF5 TNFRSF6 6.50E-06 0.0150 0.038 78% 98% 0.617
TNFRSF5 CXCR3 4.40E-08 0.0084 0.027 81% 97% 0.622
TNFRSF5 NFKB1 8.90E-06 0.0140 0.045 78% 98% 0.620
TABLE 19: 2-gene models that correctl classify LET vs. Normals/HV each with at
least 75% accuracy
LG Analysis
Gene 1 Gene 2 Classification%
Incremental Incremental
P-Value P-Value
gene 1 gene 2 LET HV/Normals R-SQ
LGALS3BP CCR10 0.0025 0.0025 77% 95% 0.512
LGALS3BP SGK 1.5E-04 1.5E-04 77% 92% 0.404
IL6ST SGK 4.1 E-04 0.012 77% 82% 0.400
IL6ST CCR10 0.012 0.042 77% 85% 0.325
IL6ST THBS1 0.0038 0.034 85% 72% 0.299
NFKB1 SGK 0.001 3.2E-03 77% 93% 0.545
NFKB1 CCR10 0.01 0.029 77% 87% 0.311
NFKB1 IFI6 0.0035 0.024 77% 93% 0.310
NFKB1 IX&M 0.0021 0.03 85% 82% 0.306
NFKB1 IL1B 4.8E-04 0.041 77% 82% 0.287
CALR SGK 8.5E-04 0.0088 77% 85% 0.359
CALR CCR10 0.014 0.017 85% 83% 0.325
CALR IL18 0.001 0.031 77% 77% 0.277
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LG Analysis
Gene 1 Gene 2 Classification%
Incremental Incremental
P-Value P-Value
gene 1 gene 2 LET HV/Normals R-SQ
CALR IF16 0.0079 0.021 77% 78% 0.342
CALR CCL2 0.0062 0.047 77% 70% 0.322
TABLE 20: 3-gene models that correctl classify LET vs. Normals/HV each with at
least 75% accuracy
LG Analysis
Gene 1 Gene 2 Gene 3 Classification%
Incremental P- Incremental P- Incremental P-
Value Value Value
gene 1 gene 2 gene 3 LET HV/Normals R-SQ
LGALS3BP SGK THBS1 7.9E-04 0.014 0.017 77% 93% 0.512
IL6ST SGK THBS1 0.0011 0.0046 0.0051 85% 93% 0.499
IL6ST SGK CALR 0.016 0.0044 0.014 77% 93% 0.558
IL6ST SGK CR1 8.OE-04 0.0052 0.047 85% 90% 0.449
IL6ST CCR10 THBS1 0.031 0.041 0.037 77% 90% 0.394
IL6ST THBS1 MMP9 0.0081 0.0081 0.031 85% 90% 0.362
NFKB1 CCR10 0.015 0.03 0.028 85% 82% 0.434
NFKB1 CCR10 IFI6 0.022 0.035 0.03 77% 88% 0.407
NFKB1 IFI6 TRIM21 0.0018 0.0028 0.012 77% 97% 0.458
NFKB1 IFI6 IL1 B 0.001 0.0079 0.012 77% 93% 0.465
NFKB1 IF16 TLR4 0.0012 0.011 0.022 85% 88% 0.371
NFKBI IF16 FCGR2B 8.4E-04 0.0064 0.019 77% 90% 0.400
NFKB1 IF16 BST1 8.2E-04 0.011 0.025 77% 88% 0.373
NFKB1 IFI6 CR1 0.0013 0.022 0.035 77% 87% 0.380
NFKBI IFI6 MMP9 0.0016 0.02 0.045 77% 85% 0.381
NFKB1 IL1 B 6.6E-04 0.012 0.013 77% 92% 0.490
NFKB1 BST1 0.0004 0.007 0.01 77% 88% 0.433
NFKB1 TLR4 7.1 E-04 0.0079 0.014 77% 88% 0.423
NFKB1 IL18 0.0018 0.013 0.034 77% 77% 0.386
NFKB1 FCAR 0.0008 0.016 0.029 85% 85% 0.421
NFKB1 FCGR2B 0.0006 0.011 0.027 77% 88% 0.407
NFKB1 CR1 0.0009 0.023 0.035 77% 88% 0.408
NFKB1 MMP9 9.7E-04 0.02 0.035 77% 87 /a 0.401
NFKB1 ICAM1 0.0012 0.015 0.046 77% 90% 0.406
NFKB1 IL1B IF16 0.001 0.012 0.0079 77% 93% 0.465
NFKB1 IL1B OASL 0.0019 0.0088 0.024 77% 88% 0.409
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LG Analysis
Gene 1 Gene 2 Gene 3 Classification%
Incremental P- Incremental P- Incremental P-
Value Value Value
gene I gene 2 gene 3 LET HV/Normals R-SQ
NFKB1 IL1 B PLSCR1 0.0022 0.011 0.048 77% 78% 0.387
CALR SGK IL6ST 0.014 0.0044 0.016 77% 93% 0.558
CALR SGK CCR10 0.0043 0.011 0.014 77% 95% 0.505
CALR SGK TROVE2 0.004 0.0034 0.031 85% 93% 0.437
CALR CCR10 0.023 0.013 0.032 77% 92% 0.460
CALR CCR10 IF16 0.027 0.021 0.027 85% 87% 0.428
CALR CCR10 TNF 0.0046 0.011 0.048 77% 92% 0.363
CALR CCR10 IL18 0.0049 0.025 0.042 77% 88% 0.384
CALR 118 0.0022 0.027 0.036 85% 85% 0.390
CALR BST1 0.0024 0.016 0.0024 77% 82% 0.397
86
